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CONSUMER HEALTH INFORMATICS AND THE MEDICALLY UNDERSERVED: 
THE ROLE OF INFORMATION TECHNOLOGY IN HEALTH INFORMATION 
ACCESS AND HEALTH COMMUNICATION IN THE UNITED STATES 

by 

SUSAN L. MOORE 
B.S., University of New Orleans, 1994 
M.S.P.H., University of Colorado Denver / Colorado School of Public Health, 2008 



A thesis submitted to the 
Faculty of the Graduate School of the 
University of Colorado in partial fulfillment 
of the requirements for the degree of 
Doctor of Philosophy 
Health and Behavioral Sciences 
2013 



This thesis for the Doctor of Philosophy degree by 
Susan L. Moore 
has been approved for the 
Health and Behavioral Sciences Program 
by 



Sheana S. Bull, Chair 
Edward P. Havranek 
Henry H. Fischer 
Andrew W. Steele 



Moore, Susan L. (Ph.D., Health and Behavioral Sciences) 

Consumer Health Informatics and the Medically Underserved: 

The Role of Information Technology in Health Information Access and Health 

Communication in the United States 

Thesis directed by Professor Sheana S. Bull. 



ABSTRACT 

This thesis describes the results of a survey conducted to explore information 
technology (IT) and health information technology utilization patterns, impact, and the 
validity of Diffusion of Innovations (Dol) theoretical principles among patients who 
receive primary health care in an urban safety net setting. Utilization in the surveyed 
population was similar to national utilization for widely adopted technologies. Less- 
commonly adopted technology use was also observed, but at rates lagging national levels, 
confirming the existence of a timeshifted digital divide. IT use was reported by 95% of 
survey respondents. Cell phone use was significantly higher than computer use 
(p<0.001), with 93% of respondents reporting cell phone use versus 71% reporting 
computer use. Significantly more people used technology for health information than for 
health communication (65% vs. 53%, p<0.001). A self-reported general health status of 
good or better was significantly associated with health information use (p=0.001). 
Distinct groups of IT adopters identified within the surveyed population showed no 
significant difference in population distribution from adoption patterns described under 
Dol theory. This finding supports both Dol theoretical applicability within a single broad 



socioeconomic stratum and the potential use of theory-based diffusion modeling to 
reduce the impact of the digital divide through tailored health informatics solutions. 



The form and content of this abstract are approved. I recommend its publication. 

Approved: Sheana S. Bull 



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ACKNOWLEDGMENTS 

No one gets there alone, and I am no exception. This endeavor could not have 
been completed without the assistance of a great many people, all of whom have my 
unending gratitude. 

First and foremost, I thank everyone who contributed to this project as a research 
participant, whether by responding to a survey or by joining in focused group discussion. 
Research depends on data, and the data they unstintingly provided was the gift that made 
this work possible. Thanks are due next to my committee chair, Sheana Bull, for her 
expert mentorship, guidance, and research advice, as well as the significant gift of her 
time; to the other members of my committee, Ed Havranek, Henry Fischer, and Andy 
Steele, for their clinical and research expertise and for their participation; and to Susan 
Dreisbach, for acting as my academic advisor until her retirement. 

I also wish to thank my colleagues at Denver Health, particularly those in the 
Department of Patient Safety and Quality, Division of Health Services Research, 
Interpreter Services, and the 21st Century Care evaluation team. Especial thanks go to 
Josh Durfee, for his invaluable assistance with risk stratification and statistical analysis; 
to Debbie Rinehart, for her advice regarding survey incentives; to Rachel Everhart, for 
her help in identifying the survey population within the data warehouse; to Amy Tobin 
and Isabel Barrera, for aid with multiple translations; and to Tracy Johnson, for her 
suggestions about survey sampling, fielding, and presenting results. 

My thanks are given as well to colleagues and friends from other fields, in 
particular Michelle Thompson Boyer, for providing both the benefit of her expertise in 
instructional design and her tireless assistance in applying thousands of labels and stamps 

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to survey envelopes, and Shannon Granville and M.E. Lasseter, for their professional 
academic and editorial review, proofreading, and copyediting. The quality of this work 
would have been poorer without their help, and I appreciate it more than I can express. 

Last but never least, I would like to thank my family, especially my parents, Larry 
Moore and Sandra Crockett Moore, and my sister, Summer Crockett Moore, and the 
communities of friends, both online and off, who have supported me throughout. These 
include the choirs of St. John's Cathedral, Denver; the sisters and brothers of that Order 
which knows the true value of the lens of science; and those who tell collaborative stories 
in and about other worlds than these. Special thanks go to Beth Kerr and Batya 
Wittenberg for going above and beyond, to Andrea Lankin for the reminder that "al shal 
be wel, and al manner of thyng shal be wele," and finally to M.E. Lasseter and Lynne A. 
McCullough, for paving the way and walking the path alongside me from start to finish. 
Both my life and this world are richer for their presence and kindness, and I am forever 
grateful. 



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TABLE OF CONTENTS 



CHAPTER 

I. INTRODUCTION 1 

Specific Aims 5 

Specific Aim 1 5 

Specific Aim 2 8 

Specific Aim 3 8 

II. BACKGROUND AND SIGNIFICANCE 10 

Quality through Technology: Establishing a National Context 10 

Consumer Health Informatics and Patient-Centered Care 13 

Consumer Health Informatics and Chronic Disease 17 

Chronic Disease and the Medically Underserved 20 

Overcoming the Digital Divide 21 

III. THEORETICAL BASIS AND FRAMEWORK 23 

Marxist Theory 23 

Antonio Gramsci: Cultural Hegemony 26 

Michel Foucault: Knowledge and Power 27 

Diffusion of Innovations 29 

IV. RESEARCH DESIGN AND METHODS 35 

Overview 35 

Study Population 35 

Inclusion and Exclusion Criteria 36 

Risk Stratification 37 

Survey Procedures 39 

Survey Design 39 

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Survey Pilot Test - Focused Group Discussion 41 

Sampling Methodology 44 

Survey Implementation 45 

Data Collection and Management 48 

Analysis Plan 50 

Measures of Interest 50 

Specific Aim 1, Research Question 1 52 

Specific Aim 1, Research Question 2 52 

Specific Aim 1, Research Question 3 52 

Specific Aim 1, Research Question 4 53 

Specific Aim 2 53 

Specific Aim 3 54 

V. RESULTS 55 

Survey Response 55 

Weighting and Balancing 62 

Analytical Methods 62 

Descriptive Statistics 63 

Technology Users, Overall 65 

Computer Users 68 

Cell Phone Users 70 

Internet Users 71 

Activity Patterns Among IT Users 73 

Health Information 77 

Health Communication 80 

Other Technology Users 82 



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Technology Nonusers 84 

Opinions about Technology 87 

Health Status and Information Technology Use 87 

Population Level 88 

Tier 1 Level 88 

Tier 2 Level 88 

Tier 3 Level 89 

Diffusion of Innovations: Technology Diffusion Assessment 89 

VI. DISCUSSION 93 

REFERENCES 107 

APPENDIX 122 

A. CHI Survey Instrument (English) 122 

B. Invitation Letter and Mailing Labels (English) 131 

C. Reminder Postcard (English) 133 

D. CHI Codebook 134 



IX 



LIST OF TABLES 



TABLE 

1: Attributes of Innovations in Terms of HIT 30 

2: Classification of Innovation Adopters 31 

3: Five Phases of the Gartner Hype Cycle 33 

4: DH Primary Care Patient Demographics, January 7, 2013 36 

5: DH Tiering Algorithm Assignment, Version 1.0 39 

6: Survey Focus Group Demographics 42 

7: Survey Pilot Test Emergent Themes by Content Area 43 

8: Survey Implementation Timeframe 45 

9: Survey Population Demographics, Unadjusted 60 

10: CDC "Healthy Days" Measures 64 

11: CDC "Healthy Days" Measures by Demographic 64 

11: CDC "Healthy Days" Measures by Demographic, continued 65 

12: Information Technology (IT) User Classification 65 

12: Information Technology (IT) User Classification, continued 66 

13: IT General User Status by Demographic 66 

13: IT General User Status by Demographic, continued 67 

14: IT Users, Technology Utilization by Demographic 68 

15: Utilization Patterns among Computer Users (n=266) 69 

16: Utilization Patterns among Cell Phone Users (n=364) 70 

16: Utilization Patterns among Cell Phone Users (n=364), continued 71 

17: Utilization Patterns among Internet Users (n=279) 72 

18: IT-Based Activities (n=405*) 74 

19: IT-Based Activities by Demographic 75 

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19: IT-Based Activities by Demographic, continued 76 

20: Utilization Patterns among Health Information Users (n=231) 77 

20: Utilization Patterns among Health Information Users (n=231), continued 78 

21: Health Information Content (n=237*) 79 

22: Utilization Patterns among Health Communicators (n=192) 80 

23: Health Communication Contacts (n=210*) 81 

24: Other Technology Utilization (n=403*) 82 

25: Other Technology Users, Utilization by Demographic 83 

26: IT and HIT Barriers and Facilitators 85 

27: IT and HIT Nonusers by Demographic 86 

28: Technology Diffusion Classification, Surveyed Population 92 



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LIST OF FIGURES 



FIGURE 

1: The Chronic Care Model 19 

2: The Digital Divide as a Marxist Construct 25 

3: The Diffusion of Innovations Distribution Curve 31 

4: Gartner Hype Cycle 34 

5: Inclusion/Exclusion Criteria Flow Diagram 37 

6: Survey Results: AAPOR Response Types 55 

7: Age Histogram and Box Distribution, Respondent vs. Sampled 61 

8: Age Q-Q plot, Respondent vs. Sampled 61 

9: Computer Adoption - Duration of Use 90 

10: Cell Phone Adoption - Duration of Use 90 

1 1 : Internet Adoption - Duration of Use 91 

12: Model and Surveyed Population Distributions 92 



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LIST OF EQUATIONS 



EQUATION 

1 : Flesch-Kincaid Grade Level Readability Formula 40 

2: Probability Sampling - Sample Size Formula 44 

3: Response Rate 56 

4: Cooperation Rate 57 

5: Refusal Rate 58 

6: Contact Rate 58 



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LIST OF ABBREVIATIONS 



Ale glycosylated hemoglobin; laboratory test that measures average 

levels of blood glucose over time and is used to both diagnose 
diabetes mellitus and assess a patient's degree of diabetes control. 
See also HbAlc. 

BP blood pressure 

AAPOR American Association for Public Opinion Research 
AHRQ Agency for Healthcare Research and Quality 

ARRA American Recovery and Reinvestment Act of 2009 

ACA Affordable Care Act (see also PPACA) 

AIM AOL Instant Messenger; chat system developed and maintained by 

AOL 

AOL online service and content provider whose corporate name is the 

acronym for the original company name of America Online 
CAHPS Consumer Assessment of Health Providers and Systems 
CCM Chronic Care Model 

CDC Centers for Disease Control and Prevention 

CDPS Chronic Illness and Disability Payment System 

CHI consumer health informatics 

COMIRB Colorado Multiple Institutional Review Board 

COPD chronic obstructive pulmonary disease 

CPOE computerized provider order entry 

DH Denver Health and Hospital Authority 

DM diabetes mellitus; diabetes 

Dol Diffusion of Innovations 

eHealth electronic health; the use of electronic or information technology 

for health and health care 
EHR electronic health record 

EMR electronic medical record 

FCC Federal Communications Commission 

FPC finite population correction 

FQHC federally qualified health center 

HbAlc hemoglobin Ale; glycosylated hemoglobin; laboratory test that 

measures average levels of blood glucose over time and is used 
both to diagnose diabetes mellitus and assess a patient's degree of 
diabetes control. See also Ale. 

HIT health information technology 

HITECH Health Information Technology for Economic and Clinical Health 
Act 

HRQOL Health Related Quality of Life 

HRSA Health Resources and Services Administration 

HTN hypertension 

ICPSR Inter-university Consortium for Political and Social Research 

ICT information and communications technologies 

IOM Institute of Medicine 



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IRC Internet Relay Chat; messaging system protocol for real-time 

synchronous one-on-one or group text-based communication over 
network channels via transmission communication protocol and 
security 

IT information technology 

LDL low-density lipoprotein; cholesterol lab test used as a measure of a 

patient's diabetes control 
mHealth mobile health; the use of mobile technology for health and health 

care 

MAR missing at random 

MCAR missing completely at random 

MCMC Markov chain Monte Carlo 

MU meaningful use 

NHIN Nationwide Health Information Network 

OMB Office of Management and Budget 

ONC Office of the National Coordinator for Health and Information 

Technology 
PHR personal health record 

PPACA Patient Protection and Affordable Care Act (20 1 0) 

SAS Statistical Analysis System; analytical software originally created 

at North Carolina State University and now developed and 

maintained by SAS Institute, Inc. (Cary, NC) 
SE standard error 

USPS United States Postal Service 

WHO World Health Organization 



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CHAPTER I 
INTRODUCTION 

In 2001, as part of its historic in-depth analysis of health care in the United States, 
the Institute of Medicine (IOM) found that information technology (IT) had the potential 
to promote the provision of health care that was "safe, effective, patient-centered, timely, 
and equitable" (1). Consumer health was identified as a specific domain in which IT 
could be of great benefit. Studies to date support this finding, indicating that consumer 
health informatics applications are capable of being used to engage patients, augment 
clinical interventions, aid in decision support, promote chronic disease self-management, 
and improve both intermediate and longer-term clinical health outcomes (2). 

Unfortunately, this approach may result in unintended negative consequences for 
those who are already significantly burdened with health disparities. The term "digital 
divide" refers to the disparity between those who have both the access and knowledge 
necessary to utilize Internet-based IT and those who do not (3). The digital divide has 
been shown to disproportionately affect members of the vulnerable and medically 
underserved "priority populations" (4, 5) traditionally served by the health care "safety 
net." The safety net refers to the system of care for patients with limited or no health 
insurance, offered by health delivery systems and providers who have committed either 
under the law or by chosen mission to care for patients regardless of their ability to pay 
(6). Safety net providers include public hospitals, federally qualified health centers 
(FQHCs), and public health departments, and their patients include racial and ethnic 
minorities, low-literacy populations, people of below-average socioeconomic status, 



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people with disabilities, children and the elderly, people with multiple chronic conditions, 
and rural populations (7-10). 

This dissertation describes the results of a cross-sectional study conducted to 
explore the use and value of IT among patients who receive primary health care in an 
urban safety net setting, considered as a representative sample of a single broad stratum 
of the current societal superstructure in the United States. These concepts were explored 
through a social science perspective, with consideration given to social and structural 
factors that affect access to and use of technology. Inequality of technological access, 
control of information within health care delivery systems, and interactions between 
knowledge and power were examined in theoretical contexts and considered within the 
Diffusion of Innovations (Dol) framework. 

The well-documented phenomenon of the digital divide can be showcased in a 
theoretical context through a class-oriented examination of the societal implications of 
technology access and use. Karl Marx first recognized the increased availability of 
technology to those with greater resources and the subsequent leveraging of technology 
to conduct capitalist processes as a way by which the worker could be further distanced 
from the means of production (11). Marx's proposal that economic change has direct 
political and cultural effects on the societal superstructure led directly to Gramsci's 
observation that cultural norms are in fact social constructs imposed by the prevailing 
class as part of the dominant ideology and accepted by subaltern groups as the natural 
state of being (cultural hegemony) (12, 13). As technology is considered to be a 
significant element in the structural formation of economic change, it must then also be 
considered a key factor in hegemonic domination. The social class-based variation in 

2 



access to and ability to effectively use technology for health informatics purposes that is 
characteristic of the digital divide represents this culturally hegemonic societal 
superstructure. This variation is one of measurable structural violence — the systematic 
exertion of violence by those who belong to a particular social order upon those who are 
members of less-privileged classes (14) — exerted here through the restriction of 
knowledge and the consequent promotion of health disparities. French historian and 
social theorist Michel Foucault (15, 16), whose social and structural critiques of health 
systems and organizational discipline have significantly informed philosophical thought 
on health and health care, has clearly described this interrelationship between knowledge 
and power. Foucault' s Birth of the Clinic describes the historical transformation of social 
and political landscapes necessary to produce the institution of modern clinical medicine 
and introduces the concept of the medical gaze and the need for attentive, objective 
observation of patients as central to a modern treatment paradigm (17). 

Limited information is currently available on the impact of consumer health 
informatics applications on health outcomes among the priority population groups 
traditionally served by the safety net (2, 18). Nonetheless, the dual lack of access to 
technology and information that is represented by the digital divide should not be 
assumed to also represent lack of interest in technology. Priority population groups, as 
defined by the Agency for Healthcare Research and Quality (AHRQ) (4), include people 
who are traditionally considered to be medically vulnerable, such as those without health 
insurance, people with low income or low socioeconomic status, racial and ethnic 
minorities, low-literacy populations, people with disabilities, children, the elderly, and 
those living in rural areas. A disproportionate share of these groups receive health care 

3 



through core safety net systems, which the IOM has characterized as health systems with 
policies and practices that call for treating patients regardless of their ability to pay (19). 
Patients in these settings have shown interest in the idea of technology-based information 
sharing with their care providers, and have expressed desire to know more about their 
own health information (20-23). 

Although the digital divide is an indicator of disparity between social classes in a 
societal superstructure, recent technological innovations may be able to help overcome 
the traditional structural inequity that this disparity perpetuates. Everett Rogers' theory of 
the Diffusion of Innovations (Dol) has identified the use of appropriate technological 
innovations for specific purposes as a means of closing gaps between groups of 
innovation adopters and would-be adopters (24). Rogers has also described an approach 
for assessing rates of innovation spread and uptake among populations. The increasing 
prevalence of certain types of user-centric technology — such as cell phones — among 
priority groups, combined with priority groups' interest in using technology to support 
information sharing, offers the opportunity to improve health information access and 
health communication through use of technological innovations that priority groups have 
already self-selected. For example, Blacks and Latinos are more likely than whites both 
to own cell phones (87% compared to 80%) and to use them for a wide variety of data 
access functions. Two-thirds of both minority populations also use their cell phones to 
access the wireless Internet (25). Examining the use of these innovations within a single 
socioeconomic stratum in the context of Dol and of a constructivist paradigm (which 
assumes that individuals actively gain and create knowledge from their own experiences) 
may help identify and develop targeted solutions that bridge the inequity gap and 

4 



improve shared decision making by empowering patients with health information that 
best meets their needs. 

Specific Aims 

The specific aims of the study were as follows: 
Specific Aim 1 

To assess and describe current methods and patterns of IT utilization for health 
information access and engagement in health communications among adult patients who 
receive care in an urban safety net setting. 
Research Question 1 

How do patterns of IT utilization in general, IT utilization for health information 
access, and IT utilization for health communications differ by demographic subgroup? 

Demographic Measures (Denver Health clinical systems): age, gender, race, 
language 

IT-General Utilization Measures (survey data): computer, cell phone, and Internet 
user status; computer, cell phone, and Internet use device type; computer, cell phone, and 
Internet use duration; computer, cell phone, and Internet use frequency; computer, cell 
phone, and Internet use importance; computer and cell phone ownership; Internet access 
speed; and IT activity type. 

IT-Health Information Measures (survey data): health information access user 
status; health information access duration; health information access frequency; health 
information access importance; and health information access topics. 



5 



IT-Health Communications Measures (survey data): health communication user 
status; health communication duration; health communication frequency; health 
communication importance; and health communication contacts. 
Research Question 2 

How do patterns of IT utilization in general, IT utilization for health information 
access, and IT utilization for health communication differ by health status? 

Health Status Measures (survey data, Denver Health clinical systems): Centers 
for Disease Control and Prevention (CDC) "Healthy Days" self-rated general health 
status, unhealthy days in past 30 days, and mental health in past 30 days; Denver Health 
risk stratification tier (version 1.0). 

IT-General Utilization Measures (survey data): computer, cell phone, and Internet 
user status; computer, cell phone, and Internet use device type; computer, cell phone, and 
Internet use duration; computer, cell phone, and Internet use frequency; computer, cell 
phone, and Internet use importance; computer and cell phone ownership; Internet access 
speed; and IT activity type. 

IT-Health Information Measures (survey data): health information access user 
status; health information access duration; health information access frequency; health 
information access importance; and health information access topics. 

IT-Health Communications Measures (survey data): health communication user 
status; health communication duration; health communication frequency; health 
communication importance; and health communication contacts. 



6 



Research Question 3 

What are the barriers and facilitators to IT utilization in general, IT utilization for 
health information access, and IT utilization for health communication among nonusers? 

IT-General Utilization Measures (survey data): computer, cell phone, and Internet 
user status; health information access user status; and health communication user status. 

Barrier Measures (survey data): computer, cell phone, and Internet use barriers; 
health information access barriers; and health communication barriers. 

Facilitator Measures (survey data): computer, cell phone, and Internet use 
facilitators; health information access facilitators; and health communication facilitators. 
Research Question 4 

What general opinions about IT and health IT (HIT) are held among adult patients 
who receive care in an urban safety net setting? 

Opinion Measures (survey data): Topics and themes emergent from participant 
responses to open-ended survey questions. 
Objectives 

The objectives of this aim were to assess the prevalence of IT use, identify 
interest in and familiarity with specific IT modalities and activities, discern patterns of 
behavior related to IT use, and identify barriers and facilitators to IT use both in general 
and for health information access and health communications among members of priority 
populations as represented by adult patients who receive care in an urban safety net 
setting. It was anticipated that safety net patients with chronic diseases would have 
greater interest in, engagement with and use of IT for health information access and 
health communications than would those without chronic illness. 

7 



Specific Aim 2 

To compare the health status of IT users and IT nonusers among adult patients 
with chronic disease who receive health care in an urban safety net setting. 
Hypothesis 

Adult patients with chronic disease who receive care in the safety net and who use 
IT to access health information and engage in health communications are predicted to 
have better health status than adult patients with chronic disease who receive care in the 
safety net and who are IT nonusers. 
Objective 

The objective of this aim was to examine the potential of consumer health 
informatics applications to improve health outcomes among patients with chronic disease 
who are members of priority populations. 
Specific Aim 3 

To evaluate the applicability of traditional Dol theory when used to examine 
patterns of adoption and utilization of HIT among adult patients who receive care in an 
urban safety net setting. 
Hypothesis 

Members of priority populations have an interest in using IT to access health 
information and to engage in health communications that is equivalent to that reported 
among members of more advantaged populations; however, they do not use the same 
types of IT in the same manner or to the same extent. 



8 



Objective 

Dol theory describes rates and patterns of identification, adoption, and utilization 
for specific technological innovations by groups of adopters who are in part characterized 
by their socioeconomic strata. This aim proposed to examine the applicability of Dol 
theory within rather than across socioeconomic strata to determine if observed patterns 
still persist when barriers to access are considered as a known factor rather than being 
used in the definition of a larger subgroup. 



9 



CHAPTER II 
BACKGROUND AND SIGNIFICANCE 
Quality through Technology: Establishing a National Context 

In 1998, the IOM created the Committee on the Quality of Health Care in 
America and charged it to develop an approach to substantially and significantly improve 
the quality of health care over the next decade. In 2001, the committee's second report, 
Crossing the Quality Chasm, called for the health care delivery system to be redesigned 
in order to improve healthcare quality as a whole (1). As part of its historic analysis, the 
committee identified 6 aims for improvement, proposing that health care should be "safe, 
effective, patient-centered, timely, and equitable." 

In its report, the committee noted the potential for IT to play a critical role in the 
transformation of the health system to achieve all 6 specified aims for quality 
improvement, and recommended that better integration of IT into health care should be 1 
of 4 key areas essential for system transformation. Specific recommendations included 
developing a national information infrastructure; promoting the adoption of electronic 
medical record (EMR) and computerized provider order entry technology (CPOE); 
establishing data standards for health information exchange; and continuing to use and 
develop informatics applications for patients, a field which has become known as 
consumer health informatics. Informatics is the science of using data, information, and 
knowledge to improve both human health and the delivery of health care services; 
consumer health informatics is the science of informatics as relates to consumer needs, 
with a focus on information structures and processes that empower consumers to manage 
their own health (26). 

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In light of the committee's recommendations, in 2004 Executive Order 13335 
(27) established the position of National Coordinator for Health Information Technology 
and charged it with responsibility for developing and leading a strategic action plan to 
actively promote HIT implementation in both public and private sectors nationwide (27, 
28). Subsequent legislation under the Health Information Technology for Economic and 
Clinical Health (HITECH) Act, included in the American Recovery and Reinvestment 
Act of 2009 (ARRA), mandated the continuation of the position of the National 
Coordinator, established the Office of the National Coordinator for Health and 
Information Technology (ONC), provided significant incentives for HIT adoption, and 
established an initial framework and a schedule for national HIT infrastructure 
development and deployment (2, 29). 

In the first year of the HITECH Act, ONC set forth the following initiatives with 
specific impact on consumer health informatics (30): 

1 .) The creation of a Nationwide Health Information Network (NHIN) as a 
secure, interoperable health information infrastructure designed to connect 
providers, consumers, and other key stakeholders involved in supporting 
health and health care. From a consumer health informatics perspective, 
NHIN is intended to provide patients with the capability to manage and 
control their own personal health records as well as providing them with 
access to their health information from clinicians' electronic health record 
(EHR) systems, while also ensuring security and confidentiality of personal 
health data. 



11 



2. ) The establishment of incentive criteria for the "meaningful use" of EHRs by 

providers who participate in Medicare and Medicaid programs. Meaningful 
use was defined to include the electronic capture of health information in 
coded formats, the use of such electronically captured information to track 
certain key clinical conditions and to assist with the coordination of care, and 
the use of health information for public health and clinical quality measure 
reporting. 

3. ) The creation of an initial set of standards, implementation specifications, and 

meaningful use certification criteria for EHRs. 

4. ) The development in cooperation with health care organizations and standards- 

development organizations of a set of interoperability specifications and 
uniform data exchange formats, along with detailed technical specifications 
for use, intended to support health information exchange between systems. 
Further expansion of the ONC initiatives under the HITECH Act was authorized 
in 2010 with the passage of the Patient Protection and Affordable Care Act (PPACA; the 
Affordable Care Act) (31). Section 1561 of the Affordable Care Act required the 
Department of Health and Human Services, in collaboration with the HIT Policy and HIT 
Standards Committees created by ONC, to develop secure, interoperable standards and 
protocols to facilitate patients' ability to electronically enroll and manage their 
participation in federal and state health and human services programs (32, 33). The 
HITECH Act also called for an update to ONC's original Federal Health Strategic Plan as 
last published in 2008. In January 2012, ONC released the updated plan, which takes into 
account changes in HIT in the last several years and establishes 5 broad goals for the 

12 



future. The fourth goal is to "empower individuals with health IT to improve their health 
and the health care system," thereby reiterating IOM's recommendation on consumer 
health informatics and emphasizing the continuing significance of enabling patients to 
access and use HIT effectively (34). 

Consumer Health Informatics and Patient-Centered Care 

Concomitant with the drive toward an increasingly technological health care 
system is a movement toward a health care model that focuses on treating the person, not 
the disease. Prior models of health care delivery were largely provider-driven, with the 
majority of decisions made by the physician and presented to the patient as directives to 
be followed, based on clinical data and provider expertise. The patient-centered approach 
involves empowered patients acting in consultation with providers as decision-makers in 
their own care experience, with health services designed to accommodate patients' 
individual preferences and health needs (35). 

The advent of the patient-centered care model brought a need for patients to be 
able to obtain essential information and resources in order to understand their health- 
related care options, to engage in complex decision making, and to receive support for 
making good health choices and managing their own health-related behaviors according 
to their individual health needs. In response, patients turned in large numbers to online 
sources of information. In 2010, searching for health information online was the third 
most frequent Internet activity, following email and general search engine use. Eight out 
of 10 Internet users in the United States reported searching for health information online, 
comprising a full 59% of all Americans (36). Online health information seekers were 



13 



more likely to be women, white, and college-educated; have higher incomes; and act as 
unpaid caregivers for other individuals (10, 36). 

To increase the availability of health information to patients, health systems and 
health care providers participating in meaningful use programs are required under Stage 1 
criteria to offer electronic copies of health information to patients within 3 days of a visit, 
and are required under Stage 2 criteria to offer patients access to health information 
online within 4 days of a visit. Electronic and online accessmay be offered through Web 
sites designated as "patient portals," where patients can access their electronic health 
records and review their health information, including data such as lab results, scheduled 
appointments, and medication refill information (30, 37-40). Early adopters of patient 
portals, such as Kaiser Permanente (KPHealthConnect) and Geisinger Health System 
(myGeisinger), have reported decreased office visits in tandem with observed 
improvements in patient-provider communication and quality of care (41). Despite 
concerns expressed about privacy and security of personal health information, patients 
continue to express willingness to engage in technology-based health information access 
and data sharing (42). 

In addition to availing themselves of online resources for health information, 
patients are increasingly using the Internet to participate in health-related community 
interaction, peer support, social engagement, and health data tracking. Web sites such as 
Livestrong, Mindbloom, and SparkPeople offer health promotion through tailored 
interfaces to engage users daily in tracking self-provided health data such as weight, body 
measurements, and physical fitness activity, along with support for wellness-focused 
behavioral change such as tobacco cessation, providing support both directly through the 

14 



user's personalized interface and through online forums and social network channels to 
promote interaction and community engagement with other users of the service (43-45). 
Sites such as PatientsLikeMe, TuDiabetes, and CancerCare offer condition-based support 
for diseases through tailored spaces where patients living with specific conditions can 
receive information and peer-to-peer support through interaction with other users facing 
similar challenges (46-48). The patient-centered concept of "participatory medicine," in 
which patients are actively encouraged to use the Internet to seek information on their 
own behalf or others' and to use their findings to inform and empower their interactions 
with their health care providers, is actively promoted both by sites such as e-P atients.net 
and through a nonprofit organization and online peer-reviewed journal (49-51). Such 
activities have a sizeable audience. Of the 74% of American adults who use the Internet, 
62% have reported using social network sites, and 23% of them reported keeping up with 
their friends' health-related site updates. More than a quarter (27%) of Americans have 
reported tracking some sort of health data online, while 18% of all online users and 23% 
percent of online users with chronic conditions reported turning to the Internet to seek out 
others with similar health conditions (36, 52). 

Consumer engagement with health informatics solutions is also not limited to 
traditional Web site interfaces. The term "Web 2.0" broadly refers to the transition of 
Web design and technology from a largely static, server-centric information repository 
(where information is distributed in broadcast fashion from the source server across a 
network to the client recipient) to an interactive model where server and client 
applications engage in synchronized data exchange and service delivery occurs through a 
shared platform. The core concept underlying Web 2.0 is one of community-sourced 

15 



development that utilizes a Web-based platform as infrastructure and shared space for 
engagement (53). This shift in conceptualization from the Web site provider as both 
content creator and distributor to users as content creators and active contributors to a 
community product is accompanied by a growing "collective intelligence," in which the 
sum of the knowledge value contributed by the community of users is greater than that 
which could be attributed to an individual alone (53, 54). The Web 2.0 framework not 
only is well suited to both patient-centric participatory medicine and a peer-to-peer 
approach to health care through social networking (55), but also provides access to shared 
platforms through social media and emergent technologies that are not dependent on 
traditional access to desktop client computers for their use. "Health 2.0" or "Medicine 
2.0" refers to the development and upgrade of consumer health informatics applications 
and services to be fully interoperable with the Web 2.0 model. These tools are targeted 
toward patients and care providers and are designed to promote increased engagement in 
social networking, collaborative operations, and apomediation within and between groups 
of users (56-58), along with greater personal choice and portability of use, including use 
on a variety of mobile devices. 

The World Health Organization (WHO) defines the use of mobile technology for 
health care, or mHealth, as a rapidly growing subset of eHealth, which encompasses all 
health services supported by information and communications technologies (ICT) (59, 
60). More than 5 billion mobile phone accounts are currently in operation worldwide, 
covering more than 85% of the world's people (60). In the United States, 82% of all 
adults own mobile phones of some kind, and 40% of them use their mobile phones to 
access the Internet (25). Moreover, 35% of American mobile phone owners possess 

16 



smartphones (e.g., Android, BlackBerry, iPhone), and of those users 87% use their 
smartphone for Internet access and 25% report that their smartphone is the device they 
use for the majority of their online activity (61). The advent of widespread and affordable 
mobile and wireless technology has rapidly-increasing potential to both further transform 
the way individuals access Internet and other technological resources and to have 
significant impact on the delivery of health care, although it remains to be seen whether 
this transformation will be sufficient to overcome the impact of the digital divide for 
those who do not engage in mobile or high-speed Internet access. 

Consumer Health Informatics and Chronic Disease 

One particular area where consumer health informatics applications have been 
shown to have compelling potential for improving the delivery of patient-centered care is 
in the treatment of chronic disease. 

The problem of chronic disease is severe. The theory of epidemiologic transition 
(62) describes 3 phases of population mortality, with "degenerative and man-made 
diseases" replacing infectious disease as the primary causes of mortality in the third 
phase. In April of 201 1, the WHO published a report on the global status of chronic 
diseases, confirming that they have become the leading cause of death worldwide (63, 
64). Fully 63% of all deaths globally in 2008 (a total of 36 million) were attributable to 
chronic disease, with the majority due to cardiovascular disease, diabetes, cancer, and 
chronic lung disease (63, 64). Global economic costs of chronic disease are estimated to 
reach $47 trillion by 2030 (65). 

The situation in the United States is no better. As of 2005, almost half of 
Americans (133 million people) had at least one chronic disease and 63 million were 

17 



living with multiple chronic diseases; these numbers are projected to reach 157 million 
and 81 million respectively by 2020 (66, 67). Chronic diseases account for 70% of 
American deaths and 78% of health care expenditures, with an economic cost of $277 
billion in 2003 alone (68, 69). Chronic diseases have also contributed to a lower average 
life expectancy for Americans over the last decade relative to gains made in other 
nations, with a loss of 33.1 million disability-adjusted life-years annually (65). 

In contrast to treatment for infectious diseases and other acute conditions, 
improving care and outcomes for chronic disease depends on patients' ability to achieve 
successful management of their disease over the long term. The Chronic Care Model 
(CCM), as developed by Wagner et al, (70-72), describes an interactive approach to 
chronic disease management that involves a mobilized community in partnership with a 
health system organized for chronic care efforts that incorporate decision support tools; 
clinical information systems that provide data for monitoring performance, facilitating 
planning, reminding patients and providers of care activities, and sharing information; a 
delivery system design that promotes effective, efficient, evidence-based and culturally 
appropriate care by health teams; and self-management support to empower and engage 
patients in their own care (73, 74). 



18 




Improved Outcomes 

Figure 1: The Chronic Care Model. Reprinted from Figure 1, Effective Clinical 
Practice, 1998, Vol 1, Chronic Disease Management: What Will It Take to Improve Care 
for Chronic Illness? by Wagner EH. Reprinted with permission. 

HIT solutions are particularly suited to supporting both health systems and 

communities in improving quality of care, process and health outcomes in all four 

identified domain elements of the chronic care model (73, 75). Studies of the use of HIT 

in chronic disease care have demonstrated reduced costs and improvements in both 

process and clinical outcomes, with particular effectiveness associated with EMRs, 

personal health records, decision support tools, computerized prompts, electronic 

scheduling, disease registries and population management (75-79). Patient-facing 

consumer health informatics solutions have also proven effective in chronic disease 

management (2), with results that include improvements in blood pressure control among 

hypertensive patients (80, 81), improvements in glycemic control among diabetic patients 

(82, 83), and improvements in medication adherence among asthmatic patients (84). 

Evidence suggests that although persons with chronic disease are less likely to have 

online access than those without (68% and 81% respectively) (85), when access is 

available, users with chronic disease are more likely than those without to both seek and 

use online information (85, 86). Among patients with chronic conditions, 75% reported 

19 



that online information affected their care decision-making, 69% reported using online 
information in discussions with their care providers, and 57% reported changing self- 
management behaviors based on information found online (87). 

Chronic Disease and the Medically Underserved 
The impact of chronic disease is most significant among low income, vulnerable 
and medically underserved priority populations (4, 5), including racial and ethnic 
minorities, low-literacy populations, people with disabilities, children and the elderly, and 
those living in rural areas. On a global scale, almost four-fifths of deaths from chronic 
disease occur in low and middle-income countries, including 80% of deaths due to 
cardiovascular disease and diabetes. Death comes earlier to these populations as well, as 
nearly a third of deaths from chronic disease in low-and middle-income countries occur 
before age 60 (63). Similar trends are reflected in the United States, where racial and 
ethnic disparities in health care persist, particularly with illnesses such as cardiovascular 
disease and cancer (88). Significantly higher prevalence of chronic disease has been 
observed among blacks, Latinos, and Asians than among whites, as well as among those 
near or below the federal poverty level as compared to those 200% or more above it (65, 
67). Disparities in chronic disease treatment are present as well: for example, an analysis 
of data between 2002 and 2007 indicated that significantly lower numbers of black and 
Latino adults, low-and middle-income adults, and uninsured adults received 
recommended diabetes care compared with white, high-income, and insured adults (89). 
The problem is further exacerbated by overall disparities in quality of care and access to 
care. Blacks and Latinos receive worse care than whites for 40% and 60% of core quality 
measures, respectively, and have more difficulty accessing care 33% and 83% of the 

20 



time, while the poor receive a lower quality of care than those with high incomes for 80% 
of core measures and have consistently worse access (89-91). 

Although the burden of chronic disease on the traditionally medically underserved 
is disproportionate and the ability of HIT to improve chronic disease management 
practices and outcomes has already been confirmed, information on the effect of 
consumer health informatics applications on health outcomes among these groups 
remains limited (2). This additional disparity represents a phenomenon commonly known 
as the digital divide. 

Overcoming the Digital Divide 

The digital divide is the term applied to the disparity between those who have 
both the means of access and the knowledge necessary to effectively use Internet-based 
information technology and those who have neither (92, 93). The digital divide has been 
shown to disproportionately affect the same groups found in medically underserved 
priority populations, including racial and ethnic minorities, low-literacy populations, 
people of below-average socioeconomic status, people with disabilities, and those living 
in rural areas (7, 8, 94, 95). 

An example of the digital divide is seen in the distribution of "broadband" 
Internet service, which is defined as Internet access through technologies that support 
data transmission at speeds much higher than those available through older, "dial-up" 
technology (96). Broadband capability is considered so important in accessing online 
information that the Federal Communications Commission (FCC) was charged in 2009 
with developing a National Broadband Plan to ensure that ensure that all Americans have 
broadband access to Internet resources (97). Users with broadband access are among the 

21 



most likely to have sought health information online (36); however, 67% of whites in 
2010 had broadband access as compared to 56% of blacks. Only 45% of users with 
annual incomes under $30,000 and 67% of those with annual incomes between $30,000 
and $50,000 had broadband access, compared to 87% of those with annual incomes over 
$75,000 (98). The disparity is so significant that the FCC has proposed comprehensive 
reform and modernization of the Lifeline/Link Up program, established in 1996 to 
support the provision of "basic communications services" to low-income Americans as 
part of the FCC's universal service mission, to also include affordable access to reliable 
broadband (99). This proposal met with significant resistance from the American public, 
including 45% of current Internet nonusers (98). Resistance such as this highlights the 
importance of considering ways to overcome the digital divide that do not depend on 
modifications to overarching technological infrastructure. 

Despite the observed resistance to the FCC's proposal, the dual lack of access and 
knowledge that characterizes the digital divide does not represent lack of receptivity for 
technology-based information sharing. Underserved patients have repeatedly shown 
active interest in use of consumer health informatics solutions and engaging in health 
information exchange with their care providers (20-22, 100-105). 



22 



CHAPTER III 
THEORETICAL BASIS AND FRAMEWORK 



To address the problem of the digital divide and its growing impact upon health 
disparities, it is critical to examine systems where the medically underserved risk further 
separation from needed medical care and health support that increasingly are being 
provided through technological channels. To that end, principles inherent in both 
traditional and neo-Marxist theoretical perspectives and in post-structuralist discourse, 
where perception is critical to informing the creation of complex meaning, were 
examined concerning the relationship between knowledge and power to gain insight into 
the systems under consideration. 

Marxist Theory 

The core of traditional Marxist theory considers labor as the unit of social 
organization (11), stemming from Marx's assertion that what humanity ascribes as 
"value" derives from an expressed relationship between the object being valued and the 
act of human labor. The connection described by Marx between the productive effort of 
labor and the process of production itself is at once both simple and complex. When 
reduced to basic terms, labor is merely a unit description of the work expended to create a 
product, using that which is termed the "means of production" in order to achieve product 
creation according to the "mode of production." The "means" refers to the inputs utilized 
for production, such as raw materials and knowledge, and the "mode" refers to the 
organized process of production itself (11, 106). From this base, Marx describes how 
societal organization to facilitate the mode of production led to society being defined in 
terms of both its economic base and its overarching superstructure, where the latter 

23 



comprises all cultural aspects of the society under consideration (107). Marx concludes 
that the continuing struggle for control over the means of production and the use and 
exchange of the resulting products eventually shapes society through ascribing greater 
social and economic power — a higher "class" — to those who emerge successful from the 
conflict. 

Having proposed this impact of production-based societal organization upon 
societal superstructure, Marx examines the implications of capitalism by analyzing the 
economic and sociopolitical factors affected through the exploitation of the worker by the 
owner of the means of production (107). He begins by defining the basic usefulness of a 
product — a "commodity" — as a "use-value," which consisted of the physical properties 
attributable to that commodity, and then proceeds to describe how a system of commerce 
in which one commodity was traded for another was structured according to "exchange- 
value," which is the assignation of relative worth between commodities. Exchange-value 
does not correspond to a one-to-one relationship between use-values; rather, the 
difference between use-value and exchange-value is ascribed to a third type of value, 
which is considered as having been added to a commodity through the production 
associated with creating the commodity. This third value is the value of the labor (1 1). 

Structural violence is inherent in a capitalist system by virtue of the established 
separation between the worker and the means of production. In this division, the worker's 
labor itself becomes the object of value instead of the commodity, and is traded for wages 
instead of for products (106). This reification of the worker into an object that exists as 
part of the production process, rather than the producer of the commodity, alienates the 
worker not only from the process but also from the perception of the worker as an 

24 



individual with intrinsic power (107). Alienation provides the opportunity for 
exploitation by the addition of surplus value to commodity products. This surplus value, 
or profit, is not returned directly to the worker, but instead is retained by the capitalist 
who owns the means of production — the technical resources necessary for the production 
process — and who pays the wages for the labor. 

Application of Marxist theoretical principles to the question at hand demonstrates 
that the HIT-based infrastructure of the current American health care system can be 
considered analogous to a capitalist exchange. In this construct, the patient represents the 
worker, health informatics applications are the means of production, and the conduct of 
health communications and the delivery of health information are the equivalent of output 
commodities. The digital divide then becomes representative of the capitalist-induced 
separation of the worker from the means of production and subsequently from direct 
access to the commodity produced. The resultant implication, then, is that the divide can 
be overcome if the patient-worker obtains access to the additional value required to 
bridge the gap — or, in other words, achieves ownership of the technological means. 



Patients Stratified Separation 




by Social Class between Patients 



Means of Access 
to Health 
information A 
Communications 



Health 
information & 
Comm unications 
(EHRfEMR data) 




Figure 2: The Digital Divide as a Marxist Construct 



25 



Antonio Gramsci: Cultural Hegemony 

However, bridging the digital divide in the construct described previously is not 
as simple as providing the patient-worker with the technological means to the end, as 
indicated by the resistance encountered among nearly half of Internet nonusers to the 
FCC's affordable access to reliable broadband service initiative. To better understand this 
seemingly counterintuitive reaction, it becomes useful to examine the proposed construct 
through the lens of cultural hegemony as formulated by Antonio Gramsci, whose Marxist 
viewpoint was informed by the German idealist philosophy of Georg Hegel and 
associated concepts of the importance of perception in defining meaning. Gramsci's 
cultural hegemony takes into account the impact of subjective contextual influences 
imposed upon patients in addition to more objective effects. 

Hegemony itself can be broadly defined as the dominant influence of one political 
or social group over another. The concept of cultural hegemony as proposed by Gramsci 
refers to the hegemonic domination not by physical force alone, but by a specific set of 
ideas and beliefs that are so internalized by those who exist under its influence that they 
become incapable of realizing its presence or consequent effect upon both behavior and 
thought (107, 108). In his Prison Notebooks, Gramsci identifies the creators of 
preeminent ideology as the capitalist entrepreneurs — the social ruling class of the 
intellectual elite — in possession of technical capacity (12). Gramsci argues that the 
objective fact of the exploited position of the worker in a capitalist society is due in large 
part to the "spontaneous consent" (12, 108) yielded by workers themselves to the 
dominant group. This consent stems from a subjective perception of powerlessness 
among the working class, concomitant with an acceptance of the ideological and social 

26 



authority held by the capitalist entrepreneurs. This culturally hegemonic belief 
perpetuates the continuation of exploitation by the intellectual elite maintaining the 
dominant ideology. In the case of resistance to the provision of broadband access, those 
without broadband are similar to members of the working class; their existence is one of 
accepted powerlessness, and they have been conditioned to resist structural change. 

Gramsci contends that this state of affairs is not an inflexible one, but instead that 
the common understanding of the working class can be altered through a "war of 
position" (109) where ideas are generated among the intellectual elite and disseminated to 
the masses through education as the means of preparing and developing intellectuals (12). 
The eventual result would be a more egalitarian social structure achieved through 
equitable distribution of the power conferred by knowledge. Although he neglects to fully 
take into account the effort necessary to overcome the inertial weight of ingrained 
structural influence when attempting to achieve social change in complex systems, the 
relationship between knowledge and power has since been further confirmed to be of 
unquestionable import in such endeavors, and thus becomes a significant consideration 
when seeking to cross the digital divide. 

Michel Foucault: Knowledge and Power 

The complex interdependent relationship between power and knowledge most 
applicable within the context of the health care setting is well articulated by Michel 
Foucault. Foucault initially describes his conceptualization of power-knowledge relations 
in Discipline and Punish: The Birth of the Prison, where he states that power produces 
knowledge and that power and knowledge are so directly related that the one implies the 
other; therefore, no knowledge exists which does not both assume and establish power, 

27 



and no power exists without the correlating presence of knowledge (110). The patients in 
a health care system are subject to the Foucauldian concept of discipline achieved 
through observation, examination and normalizing judgment, in which disciplinary 
control is exerted for individuals who fail to meet standards defined as societal norms, 
such as established acceptable levels of performance on quality indicators used as proxy 
measures for health status or chronic disease control. This is a clear example of the 
power-knowledge dyad, in that knowledge of a patient's health status then promotes the 
use of power to adjust behavior perceived as faulty in maintaining good health or to 
pursue treatment (111). 

In later interviews, Foucault admitted that his interest in and examination of the 
interwoven effects of knowledge and power was born in part from his more 
archaeological, historical work first on madness and psychiatry, then on clinical medicine 
in the eighteenth century, both of which he described as having solid scientific 
frameworks but at the same time being enmeshed in social structures (16), such that 
questions of the relationship between scientific knowledge in each field and the power 
conferred upon practitioners by the social systems became of significant concern. The 
same relationships and same questions persist today in the construct under current 
examination, where the knowledge of health information confers on the patient a degree 
of power which has the potential to be used constructively in the patient-centered care 
model. Patients can work in consultation with providers to achieve better care through a 
shared decision-making approach. Conversely, lack of health information and its 
associated health knowledge resulting from insufficient access to and ability to use the 
technological infrastructure of consumer health informatics represents the conflict 

28 



paradigm inherent to the existing societal superstructure, which results in a lesser degree 
of power available to those experiencing the digital divide. 

It is at this point in the theoretical discussion that it becomes easier to recognize 
that the disparity connoted by the digital divide, although significant, is not necessarily an 
insurmountable challenge. In addition to the Marxian solution of achieving power 
through ownership of the technological means of production and Gramsci's suggested 
approach to overcoming the deleterious effects of cultural hegemony through the 
knowledge granted by education, Foucault describes an "economy of power," (16) in 
which the effects of power relations are circulated throughout an entire social body 
through social production and social service generated by the bodies and actions of 
individuals established within a network (17). This concept suggests that disseminating 
power-knowledge to the currently disempowered through interaction within social 
networks offers a potential solution to the problem of the digital divide. 

Diffusion of Innovations 

Based on the preceding work of social scientists such as Gabriel Tarde and Georg 
Simmel, Dol theory as formulated and described by Everett Rogers has long and 
successfully been used to evaluate the spread of technological innovations, and thus holds 
promise for use in this construct to assess dissemination of the power-knowledge dyad 
through social networks of patients experiencing the digital divide, where the dyad is 
represented by the innovation of consumer health informatics technology. Diffusion 
refers to the process by which communication takes place through channels over time 
among members of a social system in order to spread awareness and use of an innovation 



29 



(24). Innovation adoption takes place in the following five-stage innovation-decision 
process: 

1. ) Knowledge. The potential adopter becomes aware of the innovation's 

existence and information about its use and potential. 

2. ) Persuasion. The potential adopter develops a positive perception of the 

innovation and discusses the innovation with others. 

3. ) Decision. The potential adopter seeks out additional information and forms an 

intent to try the innovation. 

4. ) Implementation. The potential adopter begins to use the innovation on a trial 

basis. 

5. ) Confirmation. The adopter, having recognized the benefit of the innovation, 

integrates the innovation into regular routine. 
An innovation's rate of adoption can be predicted based on individuals' 
perceptions of its 5 key attributes as determined during the innovation-decision process 
(24, 1 12). Table 1 presents the 5 attributes of innovations as potentially applied to HIT. 



Table 1: Attributes of Innovations in Terms of HIT 



Attribute 


Definition 


Sample Patient Concerns 


Relative 
Advantage 


How much better an innovation 
is in comparison to its 
predecessor idea 


Is HIT really better than memory, writing things 
down on paper, or other recordkeeping methods? 


Compatibility 


How consistent an innovation 
is with established values, 
needs, and experiences 


Will HIT allow me to do the things I want and need 
to do? Will it keep my health information safe and 
private? 


Complexity 


How difficult an innovation is 
to understand and use 


How hard is HIT to use? Will I have to spend a lot of 
time learning how to use HIT and trying to figure out 
how to make it work for me? Will this lessen time I 
can spend with my health care provider? 


Trialability 


How much an innovation can 
be experimented with on a 
limited basis 


Can I test HIT before I commit to buying and using 
it? For how long/how thoroughly? What if I don't 
like it? 


Observability 


The results of an innovation as 
visible to/perceived by other 
potential adopters 


What do other patients like me think about HIT? 



30 



Just as not all innovations are created equal, not all potential adopters are the 
same. When a successful innovation's rate of adoption is examined in terms of 
cumulative adopters over time, a signature S-shaped curve appears, which over time 
approaches normality; (24) this has been shown to hold true with respect to HIT, such as 
with providers' adoption of EMR technology (113). As shown in Table 2 and Figure 3, 
adopters can be categorized into 5 distinct groups based on where they fall along the 



distribution (24) 


Table 2: Classification of Innovation Adopters 


Category 


Description 


Innovator 


First to adopt; regarded as "venturesome" or experimental in nature 


Early Adopter 


Respected by peers; opinion leaders and role models 


Early Majority 


Adopts with careful deliberation, just before the average adopter 


Late Majority 


Skeptical adopters; may often be motivated by peer or systemic pressure 


Laggards 


May be suspicious of change and change agents; characteristically delayed 
adoption; often related to precarious economic position 



Innovators 


Early 


Early 






Late 






Adopters 


Majority 


Majority 


Laggards^--*.^^ 


2.5% 


13.5% 


34% 


34% 


16% 



x-lsd x-sd x x + sd 



Figure 3: The Diffusion of Innovations Distribution Curve. Reprinted with permission 
of Simon and Schuster Publishing Group from Figure 7.3, p 281, from the Free Press 
edition of Diffusion of Innovations, 5 th Edition. Copyright © 1995, 2003 by Everett M. 
Rogers. Copyright © 1962, 1971, 1983 by the Free Press. All rights reserved. 



31 



Although the diffusion process takes place within a social system, an association 
between socioeconomic status and adopter category has been observed due to adoption 
across social strata. Innovators and early adopters are far more likely to have greater 
access to the resources necessary to complete the innovation-decision process than 
adopters who fall into one of the later-stage categories. In fact, those who are most likely 
to be affected by the digital divide are also most likely to fall into the "laggard" adopter 
category. This same group is also more likely to discontinue use of an innovation after 
having adopted it, often because of dissatisfaction with the innovation (24). 

Foucault's economy of power is present within the Dol theoretical context as 
well, in that the adoption of an innovation within a social system tends to increase the gap 
between earlier, more socioeconomically advantaged adopters and later-stage, lower- 
resourced groups. The current adoption of consumer health informatics technology by 
groups with more education and socioeconomic resources (power-knowledge) as 
compared to the impact of the digital divide among the "laggards" is an example of this 
unintended gap-widening effect. One known approach for closing the gap is to use 
targeted channels and tailored, appropriate methods and messages to promote diffusion. 
This study will attempt to identify channels and technological methods that could help to 
bridge the digital divide by not only identifying use patterns and preferences among 
technology users, but also by assessing reasons for nonuse among patients who choose 
not to engage with consumer health informatics solutions. 

One of the limitations of using Dol theory in applied research is the presence of a 
pro-innovation bias. A frequent underlying assumption in diffusion research is that the 
innovation in question will bring sufficient value to the new adopters to be worthy of 

32 



diffusion, and thus that the innovation should not be avoided (24). Similarly, it is 
assumed that innovation diffusion is possible beginning from the point of availability and 
solely based on the potential adopter's interest and desire; it is far less clear how the 
innovation-decision process applies when the innovation is extant and interest is present, 
but resources to obtain the innovation are themselves a limiting factor to adoption. This 
study intends to address both of these limitations in Dol theory by using it as the 
framework for assessing barriers to adoption and anti-innovation sentiment toward 
consumer health informatics technology, and examining the dissemination and uptake of 
consumer health informatics technology as an innovation within a single-stratum low- 
socioeconomic population rather than across socioeconomic strata. 

The concept of a technology "hype cycle," as first characterized by technology 
analyst group Gartner, Inc., in 1995, (114) represents the progression of new technology 
through 5 phases from initial development through to stability and general acceptance, as 
shown in Table 3 and Figure 4(114): 



Table 3: Five Phases of the Gartner Hype Cycle 



Phase 


Description 


Technology Trigger 


Initial innovation development; breakthrough and 
announcement at the proof of concept or early development 
stage. 


Peak of Inflated 
Expectations 


Initial publicity and/or pilot results lead to rapid trialing and 
growth among innovators and early adopters. 


Trough of Disillusionment 


Innovation is abandoned by those who find insufficient value 
in it; core technology producers stabilize, while others leave 
the market. 


Slope of Enlightenment 


Next-generation products and iterations of the technology 
innovation are refined, leading to increased use by non- 
abandoning early adopters and additional growth among later 
adopters. 


Plateau of Productivity 


Innovation stabilizes into a mainstream technology with low 
perceived risk and widespread adoption. 



33 



expectations 




Figure 4: Gartner Hype Cycle. Reprinted from "Hype Cycle Research Methodologies" 

by Gartner, Inc., 2012. This graphic was published by Gartner, Inc. as part of a larger 
research document and should be evaluated in the context of the entire document. Gartner 
does not endorse any vendor, product or service depicted in its research publications, and 
does not advise technology users to select only those vendors with the highest ratings. 
Gartner research publications consist of the opinions of Gartner's research organization 
and should not be construed as statements of fact. Gartner disclaims all warranties, 
expressed or implied, with respect to this research, including any warranties of 
merchantability or fitness for a particular purpose. Reprinted with permission. 

Technology that reaches the mainstream and the productivity plateau phase 
characteristically is accompanied by reduced cost-to-adopt and a consequently lower 
barrier to entry. When considered from a Dol theoretical perspective, the increased 
availability of a technology innovation that has achieved the plateau phase supports the 
concept of the innovation-decision process as dependent on perceived utility and value in 
the innovation alone, such that economic barriers to adoption may no longer be 
considered a limiting factor. This study proposed that analysis of diffusion patterns 
among members of a single socioeconomic class stratum for a technology innovation that 
has achieved the plateau of productivity in the larger population will demonstrate the 
expected diffusion curve. 



34 



CHAPTER IV 
RESEARCH DESIGN AND METHODS 
Overview 

This cross-sectional study was designed to measure the variables of interest 
through a self-administered, mixed-mode survey conducted at a single point in time 
among 3 risk-stratified groups of adult patients, randomly selected from within a larger 
population of adult patients who receive primary health care in an urban safety net 
setting. 

A multiphase mixed methods approach involving both sequential and concurrent 
elements was employed for survey development and data collection (115). An integrated 
approach to mixing quantitative and qualitative data (116) was used in analysis to 
maximize the generalizability of results while also providing in-depth insight into 
patients' general opinions of and engagement with IT and patterns of behavior related to 
IT use in general, for health information access, and for health communications. The 
study was approved by the Colorado Multiple Institutional Review Board (COMIRB) as 
protocol #12-1099. 

Study Population 

Denver Health and Hospital Authority (DH) is an integrated urban safety net 
health system whose components include a 477-bed hospital and 8 primary care clinics 
that are all FQHCs. The DH system provides health care services to 25% of residents in 
the city and county of Denver, Colorado. In 2010, the DH system recorded more than 
600,000 outpatient visits, including 335,000 primary care visits, among more than 
160,000 patients. 

35 



A majority of DH patients are members of priority populations, in particular racial 
and ethnic minorities, the uninsured, and those living below the poverty line. 
Approximately 65% of DH patients are below 185% of the federal poverty level, and 
more than 50% of DH patients are uninsured. Table 4 presents demographics of the DH 
primary care population at the time of the study. 

Table 4 : DH Primary Care Patient Demographics, January 7, 2013 





Patients 


Percent 




(N = 116,999) 


(%) 




< 18 


53,759 


45.95 


18-29 


17,231 


14.73 


30-39 


13,331 


11.39 


40-49 


10,746 


9.18 


50-59 


10,813 


9.24 


60-69 


7,027 


6.01 


70-76 


2,287 


1.95 


>76 


1,805 


1.54 


Race/Ethnicity 






White 


47,384 


40.50 


Black 


17,069 


14.59 


Hispanic/Latino 


34,354 


29.36 


Asian 


3,648 


3.12 


Other/Unknown 


14,544 


12.43 




Male 


47,367 


40.48 


Female 


69,632 


59.52 



Inclusion and Exclusion Criteria 

Patients were included in the study population if they were enrolled in primary 
care at DH, spoke either English or Spanish as their primary language, and were adults 
between the ages of 18 and 76. Enrollment in primary care at DH is defined as those 
patients who have had at least 2 clinic visits in the DH system in the previous 18 months. 
Age criteria were based on Health Resources and Services Administration Diabetes 
Collaborative guidelines for diabetes management. 



36 



Patients were excluded from the sampling frame for the study population if they 
did not have both a mailing address and telephone number on record. No exclusions were 
made based on gender, race/ethnicity, or socioeconomic status. Figure 5 presents a flow 
diagram illustrating inclusion and exclusion criteria. 



Enrolled in DH primary care 
(2 visits in past 1 8 months) 
N = 116,999 



Other 
-+/ languages 
N = 9,746 



English or Spanish as primary language 
N = 107,253 



Age <18or 
► age >76 
N = 50,223 



Adults, ages 18-76 
N = 57,030 



No address/ 
* phone 
N = 1,805 



Address and phone on record 
N = 55,225 



Figure 5: Inclusion/Exclusion Criteria Flow Diagram 
Risk Stratification 

Patients in the sampling frame for the study population were risk stratified into 
risk groups, or "tiers," according to a process and algorithm developed by DH and used 



37 



under its 21st Century Care model (117, 1 18) to tailor health care delivery to patients 
according to level of need. Of the four possible tiers (1 - low risk; 2 - medium risk; 3 - 
high risk; 4 - very high risk), stratification for this study assigned patients to tiers 1-3 
only; patients that would otherwise have been identified as tier 4 were included with tier 
3 due to a small sample size limitation identified in the first attempt at classifying the 
study population. Version 1.0 of the tiering algorithm was used to risk stratify the 
population for this study; however, the tiering process continues to evolve based on 
iterative evaluation and criteria refinement. Tier assignment under the version 1.0 
algorithm is based on a combination of clinical criteria and a patient's Chronic Illness and 
Disability Payment System (CDPS) risk adjustment score (119). CDPS was developed to 
support the use of ICD-9-CM diagnosis-based burden of illness assessments to estimate 
future health expenditures and adjust state reimbursements for Medicaid populations, and 
compares well to other models used among Medicaid beneficiaries (120, 121). As such, it 
is well-suited for use in analyses involving the safety net population served by DH, which 
is predominantly composed of Medic aid-qualified patients and uninsured patients who 
will soon qualify for Medicaid coverage under the Affordable Care Act. 

Clinical criteria used for tier assignment were established diagnoses of either 
diabetes (DM) or hypertension (HTN) combined with degree of chronic disease control 
as defined by a patient's most recent indicator laboratory values for glycosylated 
hemoglobin (Ale) measurements of average blood glucose over time, low-density 
lipoprotein (LDL) measurements of cholesterol levels, and blood pressure (BP) tests. Tier 
assignment was made first according to clinical criteria, then secondarily by CDPS score 
in the absence of defining clinical criteria for assignment, as shown in Table 5 (122). 

38 



Table 5: DH Tiering Algorithm Assignment, Version 1.0 



Tier 
(Risk Group) 


Patients (%) 
(N=55,225) 


Clinical 


CDPS 
Criteria 


3 (High) 


2,525 
(4.57%) 


Diagnosis of DM and most recent Ale > 
10; or most recent systolic BP >= 160; or 
most recent diastolic BP >= 100 


Risk score >= 
7.025 


2 (Medium) 


33,484 
(60.63%) 


Diagnosis of DM and no recent Ale; or 
diagnosis of DM and no recent LDL; or 
diagnosis ot DM and most recent LDL >= 
100; or most recent Ale >= 8 but < 10; or 
most recent diastolic BP >= 90 but < 100; 
or most recent systolic BP >= 140 but < 
160; or diagnoses of both DM and HTN 


Risk score >= 
0.243932609 
but < 7.025 


1 (Low) 


19,216 
(34.80%) 


No specific clinical criteria 


Risk score < 
0.243932609 



Survey Procedures 

A survey instrument was created for this study to evaluate patterns of behavior 
related to IT use in general and for health information access and health communications; 
interest in and familiarity with specific IT modalities and activities in general and for 
health information access and health communications; and social, behavioral, and 
systemic factors that influence IT adoption and utilization. 
Survey Design 

To maximize generalizability of results in comparison to national data, survey 
items were drawn in part from existing instruments such as AHRQ's Consumer 
Assessment of Health Providers and Systems (CAHPS) item sets (123), the CDC's 
Health Related Quality of Life (HRQOL) Healthy Days core measures (124), and the 
Pew Research Center's Internet & American Life Project surveys (125). Additional 
survey items developed by the investigator were added to explore topic areas of interest 
not adequately addressed by existing instruments. Open-ended elements were included 
within questions as options in fixed-choice item sets where appropriate to support 

39 



identification of additional choices not otherwise addressed in the fixed set. Open-ended 
topical and general questions were included in addition to fixed-choice questions in order 
to elicit unstructured responses and potentially identify emergent themes that might not 
otherwise be captured. To promote readability, survey item language was adapted where 
necessary to conform to a sixth-grade literacy level according to the Flesch-Kincaid 
Grade Level formula (126, 127), given below in Equation 1: 

Equation 1: Flesch-Kincaid Grade Level Readability Formula 

GL = .39 (Average Sentence Length) + 1 1.8 (Syllables/Word) - 15.59 
where: 

Average Sentence Length = (Total # Words / Total # Sentences) 
Syllables/Word = (Total # Words / Total # Syllables) 

The layout of the survey was structured according to basic visual design 
principles of proximity, alignment, repetition, and contrast (128), with attention also 
given to creating a common layout that would perform well in both paper and electronic 
versions and which would reduce cognitive load for respondents and provide specific 
visual cues and aids to guide successful survey completion (129). Response options for 
each item were grouped in close proximity with each other and with the use of specific 
graphic elements and both vertical and horizontal negative space to distinguish each 
subgroup from another, in order to promote answer selection for each item and reduce 
question omission (129). Items were aligned in balance with each other and 
symmetrically around the optical center of each survey page. Elements such as page 
headers and number blocks, horizontal lines used to divide instructional text from 

40 



question text, vertical lines used to divide survey items into columns and subtly indicate 
flow, and stop-sign and arrow graphics used to indicate skip logic and pagination were 
repeated from page to page and section to section to promote uniformity of experience 
and pattern recognition among respondents. Sans-serif fonts were used in both paper and 
electronic formats, with differential text size and bold, italic, and underlining text 
elements used to add visual interest and provide emphasis where desired. A consistent 
color palette was used throughout, with red-green-blue and hue-saturation values drawn 
from DH marketing materials to conform with anticipated patient expectations. Although 
the paper version of the survey was printed in grayscale, the color palette used supported 
shading emphasis and distinction between elements that were comparable to a full-color 
version, without loss of visual information. A storyboarding process was used to craft and 
iteratively refine the information flow from item to item and page to page, with question 
order structured according to guidelines for creating conversational, logically ordered 
surveys (129). An instructional design advisor reviewed both draft and final layout 
versions for clarity (130). 

Survey items, instructions for completing the instrument, and contextual 
explanatory language were translated from English to Spanish by a certified medical 
interpreter. Both draft and final versions of the survey were approved by COMIRB prior 
to administration. The final print version of the survey is included as Appendix A. 
Survey Pilot Test - Focused Group Discussion 

The draft survey was piloted in a focus group of a homogenously sampled (131) 
group of individuals familiar with a wide range of information technologies and 
technologically focused activities. A total of 10 participants were recruited from among 

41 



members of an online community focused on collaborative, role -based interactive 
storytelling who were in attendance at an annual gathering in Denver, Colorado. 
Participant demographics are presented in Table 6. 



Table 6: Survey Focus Group Demographics 



Demographic 


Participants 




20-29 


6 


30-39 


2 


40-49 


2 


Gender 


Female 


9 


Male 


1 


Race/Ethnicity* 


White, Hispanic or Latino 


1 


White, non-Hispanic or Latino 


9 


Geographic Location 1 


Division 1 


1 


Division 2 


3 


Division 5 


2 


Division 6 


1 


Division 8 


1 


Division 9 


2 


As defined under Office of Management and Budget ( OMB) 
Directive 15 (132). 


^ As defined by the United States Census Bureau (133). 



Pilot administration of the survey followed by unstructured group discussion was 
held over 1 hour on January 20, 2013, with the principal investigator serving as survey 
timer and group facilitator. Average time for survey completion was calculated at 12 
minutes and 16 seconds, with a median value of 12 minutes and 6 seconds. Qualitative 
data collected included written comments provided on participants' copies of survey 
instruments, written notes of observations made by the principal investigator, and an 
audio recording of group discussion that was transcribed by the principal investigator. 
Data were subjected to content analysis using an inductive coding process interspersed 
with marginal remarks (134) to allow identification of emergent themes without 

42 



predefinition. Subjects discussed were classified as themes based on the agreement of 
multiple participants with a single concept. Themes identified during analysis and 
incorporated into survey refinements are presented in Table 7. 



Table 7: Survey Pilot Test Emergent Themes by Content Area 



Content Area 


Themes 


Layout and Flow 


• Use written instructions in addition to visual indicator elements 

• Place skip logic instructions at the top of pages, not at the bottom 

• Use different, easily recognized symbols for proceed versus skip (eg, 
arrow and stop sign) 

• Use "SKIP" instead of "TURN" when indicating movement past a page 
rather than to the next page 

• Use "if you don't" language in questions immediately following skip 
logic in order to reiterate/confirm the skip 

• Use different color and/or shading to "make your eyes be not as lazy"; 
several specific recommendations 

• Open-ended questions about other things [you] do with computers and 
cell phones needs to be on the same page with the list of activities; 
flipping back and forth is annoying 

• Include definitions and/or introductory explanations at the start of each 
section, not just at the beginning of the survey 

• Error correction: spelling, numbering, other miscellaneous 
typographical and editorial recommendations 


Wording and 
Language, General 


• Reconcile similar question wording between sections to be identical 
rather than differential, and use text element emphasis to draw attention 
to the focus of a particular section instead 

• Inclusivity recommendations: active voice, specific use of "people" not 
"patients" throughout, less formal language where possible (eg, 
"lessons on" versus "education/training on") 

• Add clear definitions of meaning and snecific examnles in context for 

J_ V V-l V-l VlVCll VIVlllllllVlU \J J. lllVLllllllg LlllVJ OUVVlllV V-/VU111L/1V O ±1.1. VVlllVA V 1 V/ 1 

broadly-encompassing terms or concepts (eg, "health," "technology," 
"use," "regular," "health information," "health communication," "talk 
to someone about health or health care") 

• Recommendation for considering "tried but couldn't" in context for 
use-related questions 


Wording and 
Language, Question- 
Specific 


• Clarifications on the list of IT activities: don't use term "social media," 
use specific well-known examples of sites/services to provide context 
for any categorical group (eg, photo sites), add "talk on phone" or 
similar for voice calls to the list 

• Clarifications on the list of health information activities: add health 
insurance, information about doctors, symptoms, use "family planning" 
in addition to/instead of "birth control" 

• Clarification on the list of health communication contacts: "religious" 
leader versus "church" leader 



43 



Sampling Methodology 

The number of completed surveys required to represent generalizable results for a 
population at a 95% confidence level and with a 5% margin of error was determined by 
probability sampling (129) as given in Equation 2: 

Equation 2: Probability Sampling - Sample Size Formula 

n, = (N P )(p)a - g) 

(N p -l)(B/Cf + (p)(l-p) 

where: 

Nj = required number of completed surveys (completed sample size needed); 
Np = total population size; 

p = proportion of population expected to choose one of two response categories 
(calculation based on two categories in order to maximize heterogeneity); 
B = margin of error 

C = Z score associated with the desired confidence level 

When calculated for the study population (N=57,030) with a 5% margin of error 
and a 95% confidence level: 

N,= (57030X.5X.5) 

(57030 - 1)(0.05/ 1.96) 2 + (.5)(.5) 

N, = 382 

Therefore, 382 was considered the minimum completed sample size necessary to 
be considered representative of the study population. 

An oversampling approach was taken to allow for all-cause nonresponse among 
intended survey recipients (eg, active refusal, passive refusal, survey not received) yet 
still achieve the necessary completed sample size to ensure generalizability. Each patient 
in the sampling frame was assigned a random seed generated by SAS software (Cary, 
NC; version 9.3). Patients were ranked in numeric order by seed, and the first 650 

44 



patients in each risk tier were selected in order to minimize sampling error. A total of 
1,950 targeted recipients were selected for survey distribution. Each recipient was 
assigned a unique survey identifier (ID), which was cross-referenced to the patient's 
unique DH medical record number and securely maintained in an electronic master file. 
Survey Implementation 

Survey implementation was conducted based on Dillman's tailored design method 
for mixed-mode surveys (129), and had 3 discrete phases: initial invitation to participate, 
survey distribution, and follow-up reminder prior to survey closure. Table 8 describes the 
overall timeframe for implementation. 



Table 8: Survey Implementation Timeframe 



Description 


Date 


Invitation letter postal mailing 


February 12,2013 


Survey postal mailing, English 


March 5, 2013 


Activation of online survey, English 


March 5, 2013 


Survey postal mailing, Spanish 


March 6,2013 


Activation of online survey, Spanish 


March 6, 2013 


Survey open/data collection start date 


March 8,2013 


Postcard reminder postal mailing 


March 27, 2013 


Deactivation of online survey, English 


April 19,2013 


Deactivation of online survey, Spanish 


April 19,2013 


Survey closure/data collection end date 


April 19,2013 


Based on estimated postal mail delivery times 



The initial invitation to participate was extended as a letter sent by postal mail, 
approved by COMIRB prior to distribution. The letter was written to include both 
informed consent for research language and specific social exchange elements intended 
to establish trust, increase the perceived benefits of response, and reduce the perceived 
costs of response (129). The letter contained information about the survey itself and how 
the recipient was selected, contact information for the principal investigator and for 
COMIRB, assurance that survey completion would incur neither cost nor obligation and 

45 



that privacy would be maintained if at all possible, and an extension of thanks for 
consideration. The invitation letter was printed on DH letterhead in either English or 
Spanish, signed by the principal investigator, and individually addressed to the survey 
recipient by name both in the letter itself and on the address label. Names and addresses 
were automatically generated from a research database created for the study (Microsoft 
Access, Redmond WA, v. 1 1.8), populated with information obtained from the DH data 
warehouse. The label design included the DH logo and a color bar across the top with the 
return address. Letters were sent in standard #10 envelopes. Sample copies of the 
invitation letter and address label are included as Appendix B. 

The survey package was sent to recipients 3 weeks following the initial invitation. 
It was mailed in a standard white 10" x 13" flat envelope with printed address labels of 
the same design as that used for the invitation mailing. The survey package included a 
printed version of the survey in either English or Spanish, a pre-addressed standard white 
9" x 12" flat return envelope with postage affixed, and a $2 cash incentive. The cover 
page of each survey was designed to include informational and social exchange elements 
to encourage survey completion. Elements included the survey title, a reminder about the 
purpose of the survey and how the recipient had been selected, instructions for either 
completing and returning the survey in the enclosed envelope or alternately for 
completing the survey online at a specified SurveyMonkey web address, the recipient's 
unique survey ID handwritten in blue ink for personalization purposes, acknowledgment 
of the importance of the recipient's opinions, mention of the cash incentive in the context 
of thanks extended, the principal investigator's name and COMIRB protocol number, and 
the DH logo. The return envelope included a label with structural design similar to those 

46 



used in the outbound mailings, but printed without color to aid recipients in 
distinguishing between envelopes. The return label also included the survey ID as a 
printed element in order to reduce the chances of unlabeled, "orphan" surveys being 
returned. No identifying information other than the survey ID was included or requested 
either on the survey itself or on the return envelope. Postage was affixed in the form of 
physical stamps with colorful designs rather than through preprinted permit or the use of 
business reply envelopes; this method has been shown to both improve response rates and 
post office processing speed (129). The survey package was assembled with careful 
attention given to how its presentation should appear to the recipient: the survey was 
nested cover-up against the flap of the response envelope, and the cash incentive was 
tucked between the flap and the survey itself in order to improve both the visual impact 
of the whole and the likelihood of the incentive not being left in the mailing envelope. 

Electronic versions of the survey in English and Spanish were activated online the 
same day as the postal mailings. Online surveys were securely fielded through 
SurveyMonkey, with standard HTML5 tags applied within the SurveyMonkey custom 
page options to create text design elements, graphic elements, page layouts, and skip 
logic that mimicked the print version of the survey as closely as possible. Online 
respondents were required to enter their unique survey ID to complete the online survey. 

The follow-up reminder was offered to non-responding survey recipients as a 
postcard sent by postal mail 3 weeks following the survey mailing. The postcard was 
designed in compliance with United States Postal Service (USPS) specifications for 
dimensions, weight, and automation-compatible layout (135), printed in black ink on blue 
card stock, and sent in both English and Spanish. It included many of the social exchange 

47 



elements previously used in the invitation letter and survey package, with variation in 
visual design used to catch attention and promote ease of cognitive processing. The 
postcard also introduced new social exchange elements through specific description of 
the value of the information requested and encouragement to recipients to call the 
principal investigator for an additional survey if the previous copy had been misplaced. A 
sample copy of the reminder postcard is included as Appendix C. 

An option to extend the survey period to allow for telephone call follow-up with 
recipients who were continued nonresponders after the reminder postcard mailing was 
included in the implementation design. This contact method was to be used if response 
rates were insufficient to support meaningful data analysis, but proved unnecessary. 
Data Collection and Management 

A survey codebook was created from the final version of the print survey 
according to Inter-university Consortium for Political and Social Research (ICPSR) 
guidelines for codebooks (136). Variable details including name, label, question text, 
answer choices, and numeric codes associated with each choice were entered in 
structured fashion, along with data entry guidelines such as skip logic, identification of 
key questions, specific coding instructions in case of multiple answer entry or other 
nonstandard cases, and additional notes where applicable. A copy of the final codebook is 
included as Appendix D. 

Based on the survey codebook, relational tables, queries, and a custom data entry 
form were created within the research database to support survey data collection. The 
data entry form was designed to store the numeric code for each question in the 
underlying table, but displayed the full wording of answer choices within the form itself 

48 



to minimize data entry errors. The form was also designed using dropdown boxes and 
limit-to-list functionality for each question, such that typographical mistakes would 
generate an error message during the data entry process rather than accepting incorrect 
data. Both fixed-choice and open-choice data collection were supported by the data entry 
form, with free-text responses entered as written. Memo field design was used to underly 
free-text response data entry to avoid character limitations associated with text fields. 
Coder notes associated with free-text responses were entered in brackets to distinguish 
them from respondent data. Responses received in Spanish were entered in Spanish; the 
Spanish responses were translated by a certified medical interpreter, with the subsequent 
English versions entered following the Spanish version of each response in the 
respondent's record. The survey ID was used as the unique identifier, and the associated 
field was designated as the nonduplicated primary key for the data entry form and 
underlying table both, with the result that an error message was generated when an 
attempt to enter duplicate data was detected. Data collected within the research database 
could be exported to one of several standard formats for import and analysis with SAS or 
other analytical software as needed. 

The electronic surveys collected through SurveyMonkey were stored securely in 
the SurveyMonkey system, with researcher-level account-based secure login access 
required to retrieve study data. No identifying information other than the survey ID was 
collection from respondents; IP address tracking was disabled for this study. Data could 
be viewed within the SurveyMonkey sytem or exported securely to one of several 
standard formats for subsequent import to SAS or the research database. 



49 



Data about the mailings themselves were also collected in tables within the 
research database. Records were kept of all responses received, including dates of survey 
responses, specifics of USPS endorsements for mail returned to sender as undeliverable 
(137), records of active refusals and survey opt-out by patients, and records of patient 
questions received and answers returned over the course of the study period. Patient 
questions received in Spanish were answered in Spanish through the aid of DH 
interpreter services. 

Mail returned to sender was recorded in the research database, then handled 
according to a process established for returned mail treatment. Each envelope was opened 
and the contents were removed; letters and surveys were securely shredded, postage-paid 
return envelopes were stored in a file in the secure research cabinet for later reclamation 
of stamps, and incentives returned were redeposited in the research account. Envelopes 
with the official USPS endorsement label attached were forwarded to the Data Integrity 
division of DH's Department of Health Information Management for handling. 

Completed paper surveys and envelopes were both marked with the dates of 
receipt and of data entry, then stored securely in a locked file cabinet. All research data 
will be maintained per Health Insurance Portability and Accountability Act regulations 
for a minimum of 7 years following study closure by COMIRB, after which it will be 
securely destroyed. 

Analysis Plan 

Measures of Interest 

Measures of interest for this study included the following, presented 
alphabetically by category: 

50 



Barrier Measures (survey data): computer, cell phone, and Internet use barriers; 
health information access barriers; health communication barriers 

Demographic Measures (DH clinical systems): age, gender, race, language 

Facilitator Measures (survey data): computer, cell phone, and Internet use 
facilitators; health information access facilitators; health communication facilitators 

Health Status Measures (survey data, DH clinical systems): CDC "Healthy Days" 
self-rated general health status, unhealthy days in past 30 days, and mental health in past 
30 days; DH risk stratification tier (version 1.0) 

IT-General Utilization Measures (survey data): computer, cell phone, and Internet 
user status; computer, cell phone, and Internet use device type; computer, cell phone, and 
Internet use duration; computer, cell phone, and Internet use frequency; computer, cell 
phone, and Internet use importance; computer and cell phone ownership; Internet access 
speed; IT activity type 

IT-Health Communications Measures (survey data): health communication user 
status; health communication duration; health communication frequency; health 
communication importance; health communication contacts 

IT-Health Information Measures (survey data): health information access user 
status; health information access duration; health information access frequency; health 
information access importance; health information access topics 

Opinion Measures (survey data): Topics and themes emergent from participant 
responses to open-ended survey questions 



51 



Univariate analyses were conducted for each of the measures of interest among all 
survey respondents. Results are described through summary presentation of frequency 
data, standard error, and variance for each measure. 
Specific Aim 1, Research Question 1 

The first specific aim of the study was to assess and describe current methods and 
patterns of IT utilization for health information access and engagement in health 
communications among adult patients who receive care in an urban safety net setting. 
The first research question associated with this specific aim was to discern how patterns 
of IT utilization in general, IT utilization for health information access, and IT utilization 
for health communications might differ by demographic subgroup. Chi-square analyses 
were conducted to assess significance for the measures of interest by age, gender, 
race/ethnicity, and primary language. 
Specific Aim 1, Research Question 2 

The second research question associated with this specific aim was to assess how 
patterns of IT utilization in general, IT utilization for health information access, and IT 
utilization for health communication may differ by health status. Regression analyses 
were conducted to assess significance for each of the measures of interest by self-rated 
general health status, unhealthy days in the past 30 days, and mental health status in the 
past 30 days. All analyses were adjusted for the effects of race/ethnicity, gender, age, and 
primary language. 

Specific Aim 1, Research Question 3 

The third research question associated with this specific aim was to examine the 
impact of identified barriers and facilitators on IT general utilization, IT utilization for 

52 



health information access, and IT utilization for health communication among self- 
identified non-users. Regression analyses were conducted to assess significance for each 
of the measures of interest, adjusting for race/ethnicity, gender, age, and patients' primary 
language. 

Specific Aim 1, Research Question 4 

The fourth research question associated with this specific aim was to determine 
what general opinions about IT and HIT are held among adult patients who receive care 
in an urban safety net setting. Free-text responses received to survey questions were 
subjected to content analysis using an analytic induction strategy in order to identify 
emergent themes and topics among responses that may not have been otherwise assessed 
by closed-ended survey items. An open, heuristic coding process was used to identify 
keywords for the development of a code list, which was used to apply coding to free-text 
responses and identify recurring topics and themes. A topic was classified as a theme if a 
minimum of 5% of respondents to open-ended items identified it as a subject of interest. 
Results of the qualitative analysis were used to improve contextual understanding of the 
quantitative results from the current study. 
Specific Aim 2 

The hypothesis for this specific aim assumed that adult patients with chronic 
disease who receive care in the safety net and who use IT to access health information 
and engage in health communications were predicted to have better health status than 
adult patients with chronic disease who receive care in the safety net and who are IT 
nonusers. 



53 



Multiple linear and logistic regression analyses were conducted to assess 
significance for health status measures among patients with chronic disease who use IT 
for health information access and health communications (intervention group) as 
compared to patients with chronic disease who do not use IT for health information 
access and health communications (control group). Chronic disease status was 
determined by risk group assignment to tier 2 or tier 3. Analyses were conducted across 
the population as a whole and within each risk tier. All analyses were adjusted by 
race/ethnicity, gender, age, and patients' primary language. 
Specific Aim 3 

The hypothesis for this specific aim assumed that members of priority populations 
have an interest in using IT to access health information and engage in health 
communications that is equivalent to that reported among members of more advantaged 
populations, but do not use the same types of IT in the same manner or to the same 
extent. 

The applicability of Dol theory within a single stratum of the larger social 
superstructure rather than across strata, as traditionally considered, was assessed by 
examining of the distribution of IT access duration resulting from the univariate analyses 
conducted for Specific Aim 1, both for each IT access type and among the study 
population as a whole. The expectation was that the distribution would align with the Dol 
adoption curve such that the 5 categories of Dol adopters could be clearly identified 
within the single stratum of the larger social superstructure represented by the members 
of priority populations participating in this study. 



54 



CHAPTER V 



RESULTS 



Survey Response 

Over the 6-week survey period, responses were received for 829 of 1,950 
surveyed individuals. Response types included completed surveys, active refusals, and 
USPS returns of undeliverable mail. The vast majority of completed surveys were 
received by mail (n=393) versus online (n=21) or by telephone (n=l). No response was 
received for 1,121 surveyed individuals. Response status was classified according to 
American Association for Public Opinion Research (AAPOR) final disposition codes for 
mail surveys of specifically named persons (138). All applicable response types are 
summarized in Figure 6 through a modified CONSORT diagram (139). 



\ 



Invited to participate in mail survey of 
specifically named persons 
(stratified random sample, n = 1,950) 

I 



Unknown eligibility, "non-interview" tn=1,510) 

* Refused by addressee (n=2) 

* Insufficient address on mail from one post 
office to another post office (n=16> 

* No mail receptacle (n=2) 

* Nothing known about respondent or 
address (n=1,121) 

* Attempted -addressee not known at place 
of address (n=68) 

* No such number (n=43> 

* No such street (n=9> 

* Vacant (n=3> 

* Unable to forward, not deliverable as 
addressed (n=89) 

* Moved, left no address (n=20) 

* Temporarily away, holding period expired (n=5) 

* Returned with forwarding information (n=1 28) 

* Returned unopened -address correction 
provided (n=1> 

* Returned opened - address correction 
provided (n=2) 

* Other (n=1> 



Eligible to participate (n=440) 



x 



-o Eligible, "interview" (n=415) 
i Returned survey, paper (n=393) 
Returned survey, online (n=21) 
Returned survey, phone (n=1) 



Eligible, "non-interview" (n=2S) 
Refusal (n=1 3) 

Blank survey; implicit refusal (n=5) 
Deceased (n=7) 



Figure 6: Survey Results: AAPOR Response Types 



55 



Four standard survey outcome rates (138) were calculated from the final response 
disposition results: response rate, cooperation rate, refusal rate, and contact rate. The 
selected standard response and cooperation rate formulas count partial completes as 
respondents. These formulas were chosen from the available options because observation 
during the data entry process indicated that the number of partial completes was very low 
and predicated in part on skip logic instructions. Standard refusal and contact rate 
formulas were selected from the available options to maximize eligibility criteria 
assumptions among recipients, neither eliminating unknowns from the equation nor 
making estimates to characterize unknown data. 

The survey response rate was calculated according to Equation 3: 

Equation 3: Response Rate 

RR2 = (I + P) 

(I + P) + (R + NC + O) + (UH + UO) 

where: 

RR2 = response rate; 

I = number of complete surveys received (code 1.1); 

P = number of partial surveys received (code 1.2); 

I + P = total number of surveys received (code 1 .0 and subcodes); 

R = number of refusals and break-offs; (code 2.1 and subcodes); 

NC = number of non-contacts (code 2.20 and subcodes); 

O = other (code 2.30 and subcodes); 

R + NC + O = total number of "non- interviews" (code 2.0 and subcodes); 
UH = unknown if household/occupied housing unit (code 3.1 and subcodes); 
UO = unknown, other (codes 3.2, 3.3, 3.4, 3.9, and subcodes); 
UH + UO = total number of cases of unknown eligibility (code 3.0 and subcodes) 



56 



When calculated for the study population: 



RR2 = 



£4151 



(415) + (25) + (1510) 



RR2 = 21.28% 



The survey cooperation rate was calculated according to Equation 4 



COOP2 = cooperation rate; 

I = number of complete surveys received (code 1.1); 

P = number of partial surveys received (code 1.2); 

I + P = total number of surveys received (code 1 .0 and subcodes) 

R = number of refusals and break-offs (code 2.20 and subcodes); 

O = other (code 2.30 and subcodes); 

When calculated for the study population: 



Equation 4: Cooperation Rate 



COOP2 = 



(I±P) 



(I + P) + R + O 



COOP2 = 



(115} 

(415)+ 18 + 7 



COOP2 = 94.32% 



The refusal rate was calculated according to Equation 5: 

Equation 5: Refusal Rate 



REF1 = 



(R) 

(I + P) + (R + NC + O) + (UH + UO) 



where: 



REF1 = refusal rate; 

I = number of complete surveys received (code 1.1); 

P = number of partial surveys received (code 1.2); 

I + P = total number of surveys received (code 1 .0 and subcodes); 

R = number of refusals and break-offs; (code 2.1 and subcodes); 

NC = number of non-contacts (code 2.20 and subcodes); 

O = other (code 2.30 and subcodes); 

R + NC + O = total number of "non- interviews" (code 2.0 and subcodes); 
UH = unknown if household/occupied housing unit (code 3.1 and subcodes); 
UO = unknown, other (codes 3.2, 3.3, 3.4, 3.9, and subcodes); 
UH + UO = total number of cases of unknown eligibility (code 3.0 and subcodes) 

When calculated for the study population: 



REF1 = 



1181 



(415) + (25) + (1510) 



REF1 = 0.92% 



The survey contact rate was calculated according to Equation 6: 



Equation 6: Contact Rate 



CON1 = 



(I + P) + R + O 



(I + P) + R + O + NC + (UH + UO) 



where: 



CON1 = contact rate; 

I = number of complete surveys received (code 1.1); 



58 



P = number of partial surveys received (code 1 .2); 

I + P = total number of surveys received (code 1 .0 and subcodes); 

R = number of refusals and break-offs; (code 2.1 and subcodes); 

NC = number of non-contacts (code 2.20 and subcodes); 

O = other (code 2.30 and subcodes); 

R + NC + O = total number of "non-interviews" (code 2.0 and subcodes); 
UH = unknown if household/occupied housing unit (code 3.1 and subcodes); 
UO = unknown, other (codes 3.2, 3.3, 3.4, 3.9, and subcodes); 
UH + UO = total number of cases of unknown eligibility (code 3.0 and subcodes) 

When calculated for the study population: 



CON1 = (415)+ 18 + 7 

(415) + 18 + 7 + + (1510) 

CON1 = 22.56% 



The number of completed surveys received (N=415) exceeds the minimum 
completed sample size threshold (N=382) established through probability sampling as 
necessary to be considered representative of the population (95% CI, ± 5% error). The 
overall response rate (21.28%), while low, is not unexpected based on declining survey 
response trends observed over decades (140), and remains comparable with response 
rates obtained for national public opinion surveys; for example, typical Pew Research 
survey response rates range from 5-15% (141). More important, multiple analyses of 
nonresponse impact on survey validity have shown that results compared between 
identical surveys fielded according to standard and rigorous methods for maximizing 
response are equally statistically valid (142, 143). Similar findings were reported in 
comparative analyses conducted for 3 state-level, health-specific surveys (144). An 
examination of potential bias among nonresponders using community-level correlates in 

59 



the state of Illinois identified higher nonresponse in areas of urbanicity and concentrated 
areas of either high affluence or high poverty (140); however, these effects can be 
presumed to be minimal in the context of this study's survey, fielded both in an urban 
setting and among a single socioeconomic stratum. 

Table 9 shows the demographics of the sample-eligible study population, the 
survey sample, and survey respondents. 



Table 9: Survey Population Demographics, Unadjusted 





Survey- 
Eligible 

(N=55,225) 


Survey- 
Eligible 

(%) 


Random 
Sample 
(N=l,950) 


Random 
Sample 

(%) 


Survey 
Completed 
(N=415) 


Survey 
Completed 

(%) 


18-29 


16,114 


29.18 


426 


21.85 


52 


12.53 


30-39 


12,129 


21.96 


384 


19.69 


70 


16.87 


40-49 


9,589 


17.36 


339 


17.38 


86 


20.72 


50-59 


9.352 


16.93 


448 


22.97 


108 


26.02 


60-69 


6,206 


11.24 


277 


14.21 


81 


19.52 


70-76 


1,835 


3.32 


76 


3.90 


18 


4.34 


Female 


38,202 


69.18 


1,264 


64.82 


277 


66.75 


Male 


17,023 


30.82 


686 


35.18 


138 


33.25 




White 


24,208 


43.84 


834 


42.77 


189 


45.54 


Latino 


16,107 


29.17 


560 


28.72 


116 


27.95 


Black 


8,250 


14.94 


343 


17.59 


62 


14.94 


Other/Unknown 


6,660 


12.06 


213 


10.92 


48 


11.56 


Language 


English 


39,641 


71.78 


1,411 


72.36 


309 


74.46 


Spanish 


15,584 


28.22 


539 


27.64 


106 


25.54 


Tier / Risk Group 


Tier 1 / Low 


19,216 


34.80 


650 


33.33 


142 


34.22 


Tier 2 / Medium 


33,484 


60.63 


650 


33.33 


132 


31.81 


Tier 3 / High 


2,525 


4.57 


650 


33.33 


141 


33.98 



Differences between population groups were assessed through chi-square 

analysis. Age was found to be significantly different between the eligible population and 

the sampled population (p<0.001), between the eligible population and the respondent 

population (p<0.001), and between the sampled population and the respondent population 

60 



(p<0.001). Figures 7 and 8 show histogram, box, and quantile-plotted age distributions 
between respondent and sampled populations. 




Figure 7: Age Histogram and Box Distribution, Respondent vs. Sampled 




Figure 8: Age Q-Q plot, Respondent vs. Sampled 



61 



Gender and race were found to be significantly different between the eligible and 
sampled populations (p<0.0001; p=0.014), but not between the eligible and respondent 
populations; therefore, respondents were considered representative of the population as a 
whole. Risk tier was significantly different between the eligible and sampled populations 
due to survey design considerations (ie, stratification, oversampling), but no difference 
was found between sampled and respondent populations. 

Weighting and Balancing 

Post-stratification weighting was applied to survey response data for tier and age 
strata in order to obtain analysis results representative of the population as a whole, 
reducing the effects of nonresponse bias and increasing precision (145-148). Weight 
computation was accomplished through sample-balancing, or raking, using an iterative 
proportional fitting approach (149-151). 

Analytical Methods 

Quantitative analyses were conducted with survey analysis means, frequency, 
regression, and logistic regression procedures (152, 153) in SAS Enterprise 9.3 (Cary, 
NC). These procedures incorporate statistical adjustments for stratified or otherwise 
complex survey designs, survey weighting, domain analysis, and variance due to missing 
data. Both stratification by tier and raked weights were explicitly included in analysis. 
Missing data for HRQOL items were imputed prior to HRQOL measure calculation, 
using Markov chain Monte Carlo methods for stochastic imputation (145). Missing data 
for subgroup classification questions ("skip logic" questions) were imputed through a 
deductive approach based on contextual responses where possible, and otherwise 
remained designated as missing and were accounted for in analysis. Variance estimation 

62 



was calculated using Taylor series linearization, with missing data assumed to be missing 
at random (MAR), but not missing completely at random (MCAR). A finite population 
correction factor was not incorporated in variance estimates, as the sampling fraction 
comprised a small enough percentage of the total population to support the use of infinite 
population assumptions. 

Qualitative analysis of free-text responses to survey questions was conducted 
using an analytic induction strategy to identify emergent themes and topics among 
responses. An open, heuristic coding process was used to identify keywords for the 
development of a code list, which was subsequently used to code free-text responses and 
identify recurring topics and themes. A topic was classified as a theme if a minimum of 
5% of respondents to open-ended items identified it as a subject of interest. 

Descriptive Statistics 

Percentages reported in all tables are based on weighted frequencies rather than 
derived from unadjusted response numbers. Both standard error and Taylor series 
variance are reported for percentage values. 

Table 10 shows the results for self-rated measures of health, including general 
(physical and mental) health, mental distress, and unhealthy days in the past 30 days. 
Almost three-quarters of the population (73.62%) perceived their general health as being 
good or better, and four- fifths (82.15%) reported themselves to be in good mental health. 
The average number of physically and/or mentally unhealthy days reported was 9.82 (SE 
0.71), with a median of 3.97 days (SE 0.81). 



63 



Table 10: CDC "Healthy Days" Measures 





Responses 
(N=415) 


Frequency 
(weighted) 


Responses 

(%) 


Std Err 

(%) 


Variance 

(%) 


Good or better 


250 


73.62 


73.62 


2.66 


7.07 


Fair or poor 


165 


26.37 


26.38 


2.66 


7.07 


Mental health in past 30 days 


Good mental health 


332 


82.14 


82.15 


2.36 


5.59 


Frequent mental distress 


83 


17.85 


17.85 


2.36 


5.59 












None (all days healthy) 


127 


35.71 


35.72 


3.19 


10.19 j 


1-7 days (1 week) 


93 


24.00 


24.00 


2.84 


8.09 


8-14 days (2 weeks) 


42 


8.46 


8.46 


1.88 


3.52 


15-21 days (3 weeks) 


45 


11.02 


11.03 


2.10 


4.41 


22-30 days (4+ weeks) 


108 


20.79 


20.79 


2.51 


6.31 



Differences by demographics of age, gender, language, and race/ethnicity were 



evaluated for all measures. Significant differences were observed in general health status 
by gender (p<0.007), by age (p<0.0001), and by race/ethnicity (p<0.021). Mental health 
status was observed to be significantly different by age (p<0.001), by gender (p<0.001), 
and by language (p<0.003). Overall unhealthy days were observed to be significantly 
different by age (p<0.003) and by language (pO.OOl). No other significant differences 
were observed by demographic (Table 1 1). 



Table 11: CDC "Healthy Days" Measures by Demographic 





General Health 


Mental Health 


Unhealthy Weeks 


N=415 


Good/ 
Better 


Fair/ 
Poor 


Good/ 
Stable 


Frequent 
Distress 


None 


1 wk 

% 


2 wk 

% 


3 wk 


4+ wk 


Age 


p<0.001* 


p<0.001* 




p=0.003* 


18-29 


26.54 


2.65 


26.61 


2.57 


11.73 


9.16 


2.35 


2.84 


3.09 


30-39 


18.91 


3.05 


19.50 


2.48 


10.17 


4.47 


2.71 


2.31 


2.32 


40-49 


12.96 


4.40 


14.57 


2.79 


6.81 


3.60 


1.22 


3.18 


2.55 


50-59 


6.46 


10.47 


10.30 


6.63 


3.61 


3.15 


0.98 


1.37 


7.82 


60-69 


6.92 


4.32 


8.19 


3.05 


2.69 


2.85 


0.87 


0.85 


3.98 


70-76 


1.83 


1.49 


2.98 


0.34 


0.71 


0.77 


0.32 


0.48 


1.03 


Gender 


p=0.007* 


p<0.001* 


p=0.909 


Female 


59.26 


17.34 


62.91 


13.69 


27.84 


18.93 


6.53 


8.33 


14.96 


Male 


14.36 


9.04 


19.24 


4.16 


7.88 


5.07 


1.93 


2.69 


5.84 



64 



Table 11: CDC "Healthy Days" Measures by Demographic, continued 





General Health 


Mental Health 


Unhealthy Weeks 




Good/ 


Fair/ 


Good/ 


Frequent 












N=415 


Better 


Poor 


Stable 


Distress 


None 


1 wk 


2 wk 


3 wk 


4+ wk 




% 


% 


% 


% 


% 


% 


% 


% 


% 


Race/Ethnicity 


p=0.021* 


P= 


0.083 


p=0.112 


White 


32.04 


11.58 


35.20 


8.41 


13.21 


10.23 


5.11 


5.20 


9.86 


Hispanic/Latino 


20.16 


8.13 


23.77 


4.51 


13.20 


6.22 


1.98 


2.18 


4.71 


Black 


9.39 


5.65 


10.77 


4.28 


2.77 


3.85 


1.24 


2.65 


4.53 


Other/Unknown 


12.04 


1.02 


12.41 


0.65 


6.53 


3.71 


0.13 


1.00 


1.69 


Language 


p=0.388 


p=0.003* 




p<0.001 


* 




L English 


52.24 


17.21 


54.13 


15.32 


18.88 


19.52 


6.44 


9.45 


15.17 


Spanish 


21.38 


9.17 


28.02 


2.53 


16.84 


4.49 


2.02 


1.58 


5.63 


^Significant p-value, 0.05 or less. 



Technology Users, Overall 

Table 12 presents the IT user status results. Among the overall population, 
94.57% of people classified themselves as users of some kind of IT, whether computer, 
cell phone, or both. Cell phone use was significantly higher than computer use (p<0.001), 
with 92.70% of people reporting cell phone use versus 71.41% reporting computer use. 
Almost three-quarters use the Internet (73.62%). Significantly more people use 
technology for health information than for health communication (65.25% vs. 52.61%, 
pO.001). 



Table 12: Information Technology (IT) User Classification 



IT user status, general 


Responses 


Frequency 


Responses 


Std Err 


Variance 




(N=412) 


(weighted) 


(%) 


(%) 


(%) 


Computer only 


14 


1.86 


1.87 


0.74 


0.55 


Cell phone only 


112 


22.63 


22.76 


2.67 


7.13 


Both computer and cell phone 


252 


69.55 


69.94 


2.86 


8.27 


Neither computer nor cell phone 


34 


5.40 


5.43 


1.22 


1.49 


Computer use 










Uses computer 


266 


71.41 


71.41 


2.83 


7.99 


Does not use computer 


149 


25.58 


28.89 


2.83 


7.99 


Cell phone use 


Responses 


Frequency 


Responses 


Std Err 


Variance 




(N=412) 


(weighted) 


(%) 


(%) 


(%) 


Uses cell phone 


364 


92.18 


92.70 


1.42 


2.01 


Does not use cell phone 


48 


7.26 


7.30 


1.42 


2.01 



65 



Table 12; Information Technology (IT) User Classification, continued 



Internet use 


Responses 
(JN=4U4) 


Frequency 
(weighted) 


Responses 

(%) 


Std Err 

(%) 


Variance 

( frf \ 

(%) 


Uses Internet 


279 


73.62 


76.64 


2.65 


7.00 


Does not use Internet 


125 


22.44 


23.36 


2.65 


7.00 


Health information use 


Responses 


Frequency 


Responses 


Std Err 


Variance 




(N=410) 


(weighted) 


(%) 


(%) 


(%) 


Uses IT tor mtormation about 


231 


64.23 


65.25 


3.04 


9.24 


health-related topics 












Does not use IT for information 


179 


34.21 


34.75 


3.04 


9.24 


about health-related topics 












Health communication use 


Responses 


Frequency 


Responses 


Std Err 


Variance 














Uses IT to talk to others about 


192 


49.56 


52.61 


3.39 


11.47 


health-related topics 












Does not use IT to talk to others 


203 


44.64 


47.39 


3.39 


11.47 


about health-related topics 













Differences by demographics of age, gender, language, and race/ethnicity were 



assessed for all measures. No one below age 40 was found to be a technology "nonuser," 
but used either or both computers and cell phones. Very few people used computers but 
not cell phones — below 5% in all categories. English speakers were significantly more 
likely than Spanish speakers to use both computers and cell phones, (78.22% vs 50.83%, 
p<0.001), while Spanish speakers were significantly more likely than English speakers to 
use cell phones only (38.46% vs. 15.95%, pO.001). Table 13 shows further results. 



Table 13: IT General User Status by Demographic 





Computer 

% (row%) 


Cell 
Phone 

% (row%) 


Computer 
and Cell 


Nonuser 


Age 




p—indeti 


irminate 




18-29 


0.08 (0.26) 


3.93 (13.40) 


25.33 (86.33) 


0(0) 


30-39 


0.55(2.51) 


5.02 (26.27) 


15.73 (71.22) 


0(0) 


40-49 


0.51 (3.00) 


3.79 (22.39) 


12.34 (72.88) 


0.29(1.74) 


50-59 


0.22(1.28) 


3.80 (22.36) 


10.90 (64.11) 


2.08 (12.25) 


60-69 


0.17(1.46) 


4.45 (39.34) 


5.18 (45.86) 


1.51 (13.33) 


70-76 


0.35 (10.35) 


0.98 (29.48) 


0.46(13.76) 


1.55 (46.41) 


Gender 


p=0.191 


Female 


0.82(1.07) 


16.48 (21.54) 


54.76 (71.59) 


4.43 (5.80) 


Male 


1.05 (4.45) 


6.28 (26.71) 


15.18 (64.59) 


1.00 (4.24) 



66 



Table 13: IT General User Status by Demographic, continued 





Computer 


Cell 


Computer 


Nonuser 






Phone 


and Cell 




AT— /I J < 

IS— 41 D 


% (row%) 


% (row%) 


% (row%) 


% (row%) 


Kace/ n,tniiicity 


p— indeterminate 


wniie 


0.91 (2.09) 


8.23 (18.78) 


32.25 (73.58) 


2.43 (5.55) 


Hispanic/Latino 


0.63 (2.27) 


8.98 (32.11) 


15.74 (56.30) 


2.61 (9.33) 


Black 


0(0) 


2.67 (17.70) 


12.37 (81.98) 


0.05 (0.33) 


Other/Unknown 


0.32 (2.43) 


2.88 (21.94) 


9.59 (73.01) 


0.34 (2.62) 


Language 


p<0.001* 


L English 


0.94(1.35) 


11.13 (15.95) 


54.58 (78.22) 


3.12(4.47) 


Spanish 


0.93 (3.06) 


11.63 (38.46) 


15.37 (50.83) 


2.31 (7.65) 



Among IT users, significant differences were found by age for all types of use 
(computer, cell phone, internet, health information, and health communication). 
Utilization decreased as age increased for all types of use except health communication, 
where utilization between ages 30 and 49 was found to be higher than in any other age 
bracket (p=0.012). Women were significantly more likely than men to both seek out 
health information (p=0.027) and engage in health communication (p<0.001). Three- 
quarters of women (70.27%) and half of men (49.1 1%) looked up health information, and 
more than half (56.32%) of women compared to less than half (40.55%) of men engaged 
in health communication. Race and ethnicity were found to be significantly different for 
all types of use except health communication. Blacks were most likely to use computers 
(81.76%, p=0.022), cell phones (99.67%, p=0.024), and the Internet (84.93%, p=0.018). 
English speakers were more likely than Spanish speakers to use computers (79.50% vs. 
53.03%, pO.OOl), the Internet (82.95% vs. 60.65%, pO.OOl), and to look up health 
information (69.78% vs. 54.41%, p=0.027). Table 14 provides additional details. 



67 



Table 14: IT Users, Technology Utilization by Demographic 





Computer 


Cell Phone 


Internet 


Health Info 


Health 




















Comm 




Yes 


No 


Yes 


No 


Yes 


No 


Yes 


No 


Yes 


No 


N=415 


% 


% 


% 


% 


% 


% 


% 


% 




% 


Age 


p<0.001* 


p<0.001* 


p<0.001* 


p<0.001* 


p=0.012* 


18-29 


86.60 


13.40 


99.74 


0.26 


90.67 


9.33 


82.39 


17.61 


47.95 


52.05 


30-39 


73.73 


26.27 


97.49 


2.51 


83.03 


16.97 


71.53 


28.47 


66.37 


33.62 


40-49 


73.58 


26.42 


95.27 


4.73 


83.84 


16.16 


63.16 


36.83 


63.79 


36.21 


50-59 


65.28 


34.72 


86.47 


13.53 


61.79 


38.21 


45.75 


54.25 


45.01 


54.99 


60-69 


47.33 


52.67 


85.21 


14.79 


55.17 


44.83 


53.40 


46.60 


45.79 


54.21 


70-76 


24.11 


75.88 


43.24 


56.76 


24.90 


75.01 


20.71 


79.29 


13.76 


86.24 


Gender 


p=0.603 


p<0.569 


p=0.530 


p=0.001* 


p=0.027* 


Female 


72.16 


27.84 


93.13 


6.87 


77.46 


22.54 


70.27 


29.73 


56.32 


43.68 


Male 


68.96 


31.04 


91.31 


8.69 


73.81 


26.19 


49.12 


50.88 


40.55 


59.45 


Race/Ethnicity 


p=0.022* 


p=0.024* 


p=0.018* 


p=0.002* 


p=0.133 


White 


75.62 


24.38 


92.37 


7.63 


79.34 


20.66 


66.16 


33.84 


51.34 


48.66 


Hispanic/Latino 


57.56 


42.44 


88.40 


11.60 


63.89 


36.11 


52.82 


47.18 


44.32 


55.68 


Black 


81.76 


18.24 


99.67 


0.33 


84.93 


15.07 


64.83 


35.17 


56.56 


43.44 


Other/Unknown 


75.44 


24.56 


94.95 


5.05 


84.37 


15.63 


88.65 


11.35 


69.01 


30.99 


Language 


p<0.001* 


p=0.121 


p<0.001* 


p=0.027* 


p=0.30 


English 


79.50 


20.50 


94.18 


5.82 


82.95 


17.05 


69.78 


30.22 


54.92 


45.08 


Spanish 


53.03 


46.97 


89.29 


10.71 


60.65 


39.35 


54.41 


45.59 


46.87 


53.13 


^Significant p-value, 0.05 or less. 



Computer Users 

Table 15 presents utilization pattern results for computer users. Two-thirds of 
people use a laptop or notebook computer (67.76%), versus just over half who use a 
desktop (56.26%) and a quarter who use a tablet device (23.22). More people use just 1 
type of computer (62.63%) than use multiple types (36.54%). The majority of people who 
classified themselves as computer users own at least 1 computer (82.42%), use it daily 
(62.21%), and believe that computer use is always or usually important (68.41%). 



68 



Table 15: Utilization Patterns among Computer Users (n=266) 



Computer device type 


Responses 


Frequency 


Responses 


Std Err 


Variance 


(n >266 due to multiple device use) 


(N=266) 


(weighted) 


(%) 


(%) 


(%) 


Desktop 


163 


40.17 


56.26 


4.03 


16.26 


Laptop or notebook 


160 


48.38 


67.76 


3.67 


13.44 


Tablet 


53 


16.58 


23.22 


3.55 


12.63 


Other device 


18 


3.63 


5.08 


1.51 


2.28 


Multiple computer usage 


Responses 


Frequency 


Responses 


Std Err 


Variance 




(N=266) 


(weighted) 


(%) 


(%) 


(%) 


1 type of computer 


171 


44.72 


62.63 


3.91 


15.26 


2 types of computer 


66 


17.02 


23.84 


3.34 


11.13 


3 types of computer 


25 


8.64 


12.11 


2.85 


8.12 


4+ types of computer 


4 


1.01 


0.59 


0.82 


0.67 


Computer use duration 


Responses 


Frequency 


Responses 


Std Err 


Variance 




(N=266) 


(weighted) 


(%) 


(%) 


(%) 


< 1 month 


6 


2.14 


3.00 


1.83 


3.33 


1-6 months 


11 


3.93 


5.50 


1.91 


3.66 


7-12 months 


8 


1.97 


2.76 


1.20 


1.45 


1-2 years 


21 


4.80 


6.73 


1.82 


3.32 


3-5 years 


34 


11.35 


15.90 


3.18 


10.14 


L 5-10 years 


48 


13.53 


18.94 


3.16 


9.97 


> 10 years 


138 


33.69 


47.17 


4.00 


15.98 


Computer use frequency 


Responses 


Frequency 


Responses 


Std Err 


Variance 




(N=266) 


(weighted) 


(%) 


(%) 


(%) 


Daily, multiple times 


127 


34.54 


48.37 


4.03 


16.27 


Daily, once per day 


39 


9.63 


13.84 


2.70 


7.29 


Weekly, 3-5 days per week 


35 


9.09 


12.73 


2.58 


6.64 


Weekly, 1-2 days per week 


30 


8.26 


11.56 


2.48 


6.17 


Monthly, every few weeks 


17 


5.34 


7.47 


2.13 


4.52 


Monthly, once per month or less 


18 


4.56 


6.38 


2.19 


4.82 


Computer use value 


Responses 


Frequency 


Responses 


Std Err 


Variance 




(N=266) 


(weighted) 


(%) 


(%) 


(%) 


Always important 


122 


32.73 


t _ 45.84 J 


4.00 


16.05 


Usually important 


55 


16.12 


22.57 


3.49 


12.19 


Sometimes important 


57 


15.80 


22.13 


3.41 


11.62 


Rarely important 


19 


4.04 


5.65 


1.57 


2.46 


Not important 


13 


2.72 


3.81 


1.61 


2.62 


Computer access 


Responses 


Frequency 


Responses 


Std Err 


Variance 


Owns a computer 


217 


58.85 


82.42 


3.24 


10.49 


Does not own a computer 


49 


12.56 


17.58 


3.24 


10.49 



Multiple regression analysis was conducted for each of these measures to examine 



the significance of general health status, mental health status, and unhealthy days in the 
last month, adjusting for demographic variables of age, gender, race/ethnicity, and 
language. Value of computer use was associated with language and all 3 health 
indicators: general health status, mental health status, and unhealthy days in the past 



month. Computer ownership was associated with general and mental health status. 
Frequency of computer use was associated with general health status and language. 
Duration of computer use was associated with general health status, age, other/unknown 
race/ethnicity, and language. Desktop and laptop use were found to be significantly 
associated with age and white and Hispanic/Latino race/ethnicity. Tablet use was 
associated with Hispanic/Latino race/ethnicity. No other significant associations were 
found. 

Cell Phone Users 

Table 16 presents utilization pattern results for cell phone users. Cell phone use is 
split almost evenly between smart phones (50.70%) and regular cell phones (47.40%). 
Most people use only 1 type of cell phone (96.98%), and most people who classified 
themselves as cell phone users own their cell phones (95.97%), use them daily (87.76%), 
and believe that cell phone use is usually or always important (87.65%). 



Table 16: Utilization Patterns among Cell 


'hone Users (n=364) 


Cell phone type 

(n >363 due to multiple device use) 


Responses 
(N=363) 


Frequency 
(weighted) 


Responses 

(%) 


Std Err 

(%) 


Variance 

(%) 


Smart phone 


148 


46.72 


50.70 


3.46 


11.99 


RegulaiTbasic phone 


207 


43.68 


47.40 


3.45 


11.93 


Other cell phone 


19 


4.22 


4.92 


1.59 


2.54 


Multiple phone usage 


Responses 
(N=342) 


Frequency 
(weighted) 


Responses 

(%) 


Std Err 

(%) 


Variance 

(%) 


1 type of cell phone 


330 


83.08 


96.98 


1.36 


1.84 


2 types of cell phone 


12 


2.59 


3.01 


1.36 


1.84 


Cell phone use duration 










< 1 month 


28 


9.81 


10.68 


2.35 


5.52 


1-6 months 


12 


3.24 


3.52 


1.37 


1.89 


7-12 months 


7 


1.05 


1.14 


0.72 


0.51 


1-2 years 


29 


4.68 


5.09 


1.31 


1.72 


3-5 years 


70 


16.57 


18.04 


2.71 


7.33 


5-10 years 


111 


28.24 


30.74 


3.26 


10.62 


> 10 years 


106 


28.28 


30.79 


3.17 


10.03 



70 



Table 16: Utilization Patterns among Cell Phone Users (n=364), continued 



Cell phone use frequency 


Responses 


Frequency 


Responses 


Std Err 


Variance 




(N=364) 


(weighted) 


(%) 


(%) 


(%) 


Daily, multiple times 


268 


73.69 


79.94 


2.71 


7.36 


Daily, once per day 


41 


7.21 


7.82 


1.87 


3.51 


Weekly, 3-5 days per week 


20 


4.43 


4.80 


1.48 


2.19 


Weekly, 1-2 days per week 


20 


4.47 


4.85 


1.48 


2.19 


Monthly, every few weeks 


9 


1.32 


1.43 


0.61 


0.37 


Monthly, once per month or less 


6 


1.07 


1.17 


0.55 


0.30 


Cell phone use value 


Responses 


Frequency 


Responses 


Std Err 


Variance 




(N=362) 




(%) 


(%) 


(%) 


Always important 


222 


62.40 


68.20 


3.17 


10.07 


Usually important 


70 


17.79 


19.45 


2.84 


8.07 


Sometimes important 


42 


6.46 


7.06 


1.43 


2.04 


Rarely important 


22 


4.15 


4.53 


1.24 


1.54 


Not important 


6 


0.70 


0.76 


0.41 


0.16 


Cell phone use access 


Responses 


Frequency 


Responses 


Std Err 


Variance 




(N=339) 


(weighted) 


(%) 


(%) 


(%) 


Owns a cell phone 


315 


81.39 


95.97 


1.20 


1.43 


Does not own a cell phone 


24 


3.42 


4.03 


1.20 


1.43 



Multiple regression analysis was conducted for each of these measures to examine 



the significance of general health status, mental health status, and unhealthy days in the 
last month, adjusting for demographic variables of age, gender, race/ethnicity, and 
language. Unhealthy days in the past month was associated with smartphone use together 
with age and language; with regular cell phone use along with gender, age, and language; 
and with frequency of cell phone use together with age. Multiple device use was found to 
be significantly associated with black and other/unknown race/ethnicity. Duration of cell 
phone use was associated with language. Cell phone ownership was associated with 
other/unknown race/ethnicity. No other significant associations were found. 
Internet Users 

Table 17 presents utilization pattern results for Internet users. More people access 
the Internet on a portable computer (laptop or tablet, 66.21%) or cell phone (56.53%) 
than with a desktop computer (55.10%). Users were more likely to use multiple methods 
of access (57.85%) than a single method (42.14%). Most people who classified 



themselves as Internet users have broadband access (73.35%), go online daily (77.07%), 
and believe that Internet access is usually or always important (70.75%). 



Table 17: Utilization Patterns among Internet Users ( 


n=279) 


Internet access method 


Responses 


Frequency 


Responses 


Std Err 


Variance 


(n >279 due to multiple method use) 


(N=279) 


(weighted) 


(%) 


(%) 


(%) 


Desktop computer 


168 


40.56 


55.10 


3.97 


15.79 


Portable computer 


166 


48.74 


66.21 


3.70 


13.66 


Cell phone 


132 


41.62 


56.53 


3.86 


14.90 


Other method 


9 


1.00 


1.36 


0.75 


0.56 


Multiple access methods 


Responses 


Frequency 


Responses 


Std Err 


Variance 




(N=279) 


(weighted) 


(%) 


(%) 


(%) 


1 method 


135 


31.03 


42.14 


3.87 


14.97 


2 methods 


92 


26.89 


36.52 


3.86 


14.87 


3 methods 


52 


15.70 


21.33 


3.31 


10.96 


Internet use duration 


Responses 


Frequency 


Responses 


Std Err 


Variance 




(N=276) 


(weighted) 


(%) 


(%) 


(%) 


< 1 month 


10 


3.74 


5.13 


2.05 


4.22 


1-6 months 


13 


3.90 


5.36 


1.84 


3.37 


7-12 months 


14 


4.32 


5.93 


1.82 


3.34 


1-2 years 


23 


4.19 j 


5.74 


1.55 


2.41 


3-5 years 


26 


7.03 


9.65 


2.37 


5.62 


5-10 years 


71 


20.75 


28.47 


3.75 


14.06 


> 1 years 


119 


28.95 


39.72 


3.81 


14.52 


Internet use frequency 


Responses 


Frequency 


Responses 


Std Err 


Variance 


Daily, multiple times 


150 


43.42 


59.40 


3.82 


14.58 


Daily, once per day 


44 


12.92 


17.67 


3.20 


10.24 


Weekly, 3-5 days per week 


28 


5.20 


7.11 


1.77 


3.14 


Weekly, 1-2 days per week 


23 


6.10 


8.35 


2.14 


4.60 


Monthly, every few weeks 


13 


3.55 


4.85 


1.53 


2.34 


Monthly, once per month or less 


19 


1.92 


2.63 


0.92 


0.84 


Internet access value 


Responses 


Frequency 


Responses 


Std Err 


Variance 




(N=277) 


(weighted) 


(%) 


(%) 


(%) 


Always important 


132 


36.80 


50.33 


3.96 


15.69 


Usually important 


63 


14.93 


20.42 


3.27 


10.66 


Sometimes important 


57 


16.13 


22.07 


3.42 


11.67 


Rarely important 


17 


4.26 


5.83 


1.86 


3.48 


Not important 


8 


0.99 


1.36 


0.75 


0.56 


Internet access type 


Responses 


Frequency 


Responses 


Std Err 


Variance 




(N=276) 


(weighted) 


(%) 


(%) 


(%) 


Broadband (high speed) 


196 


53.46 


73.35 


3.55 


12.63 


Non-broadband (low speed) 


50 


11.20 


15.37 


2.81 


7.89 


Unknown 


30 


8.22 


11.28 


2.73 


7.43 



72 



Multiple regression analysis was conducted for each of these measures to examine 
the significance of general health status, mental health status, and unhealthy days in the 
last month, adjusting for demographic variables of age, gender, race/ethnicity, and 
language. Duration of Internet use was associated with general health status, age, and 
language. Using multiple devices for Internet access was associated with age, language, 
and white race/ethnicity. Desktop computer access was associated with age; portable 
computer access (laptop, notebook, tablet) was associated with age, language, and white 
and Latino race/ethnicity. Cell phone Internet access was associated with age and white 
race. Frequency of Internet use was associated with age and language. High-speed access 
was associated with language. No other significant associations were found. 
Activity Patterns Among IT Users 

Utilization pattern results for the use of IT for common activities are presented in 
Table 18. Users were significantly more likely to send and receive text messages than to 
send and receive email (84.42% vs 72.72%, p<0.001). Users were also significantly more 
likely to video chat or Skype with someone than to use direct text-based chat (42.10% vs. 
30.39%, p<0.001). Social media was not commonly utilized, with by far the highest 
activity being on Facebook (57.10% vs. 25.44% for the next highest). 



73 



Table 18: IT-Based Activities (n=405*) 



Activity 


Computer 


Cell 


Both 


User 


Nonuser 


SE, % 


Var, % 




% (n) 


% (n) 


% (n) 


% (n) 








Communication 


Email, send/receive 


29.53 


9.96 


33.23 


72.72 


27.28 


2.91-3.20 


4.55- 




(114) 


(34) 


(110) 


(258) 


(126) 




10.26 


Text, send/receive 


0.83 


75.05 


8.54 


84.42 


15.58 


0.46-2.68 


0.21-7.21 




(9) 


(253) 


(34) 


(296) 


(95) 






Voice calls, make/receive 


1.73 


74.34 


8.96 


85.03 


14.97 


0.75-2.82 


0.57-7.97 




(12) 


(251) 


(31) 


(294) 


(82) 






Chat, video/Skype 


26.39 


7.74 


7.97 


42.10 


57.90 


1.94-3.48 


3.76- 




(74) 


(20) 


(21) 


(115) 


(254) 




12.10 


Chat, direct text 


17.60 


6.26 


6.54 


30.39 


69.61 


1.59-3.38 


2.53- 




(48) 


(17) 


(25) 


(90) 


(283) 




11.43 


Chat, group text 


7.42 


4.44 


4.76 


16.62 


83.38 


1.29-2.46 


1.66-6.06 




(32) 


(12) 


(15) 


(59) 


(311) 






Information 


News 


26.89 


17.36 


20.30 


64.55 


35.45 


2.59-3.23 


6.72- 




(107) 


(47) 


(72) 


(226) 


(157) 




10.45 


Information 


29.72 


2.88 


3.26 


76.13 


23.87 


2.82-3.41 


7.94- 




(108) 


(35) 


(94) 


(237) 


(113) 




11.65 


Multimedia 


TV/movies 


30.81 


6.21 


13.92 


50.94 


49.06 


1.64-3.43 


2.68- 




(107) 


(22) 


(41) 


(170) 


(202) 




11.78 


Videos, watch 


23.79 


12.90 


25.29 


61.98 


38.02 


2.51-3.22 


6.30- 




(76) 


(38) 


(77) 


(191) 


(190) 




10.37 


Videos, make/post 


6.79 


8.93 


8.20 


23.92 


76.08 


1.53-3.01 


2.33-9.08 




(28) 


(25) 


(20) 


(73) 


(301) 






Music, listen/play 


20.75 


22.19 


26.64 


69.58 


30.42 


2.82-3.09 


7.96-9.57 




(75) 


(68) 


(80) 


(223) 


(162) 






Photos, online 


6.74 


12.04 


7.30 


26.09 


73.91 


1.91-3.27 


3.64- 




(18) 


(30) 


(18) 


(66) 


(310) 




10.72 


Gaming, solo 


14.38 


20.28 


13.71 


48.37 


51.63 


2.46-3.51 


5.09- 




(58) 


(54) 


(47) 


(159) 


(216) 




12.30 


Social media 


Facebook 


19.97 


15.04 


22.10 


57.10 


42.90 


2.80-3.36 


7.86- 




(70) 


(34) 


(66) 


(170) 


(205) 




11.29 


Twitter 


4.65 


7.77 


6.02 


18.44 


81.56 


1.37-2.62 


1.88-6.89 




(19) 


(22) 


(20) 


(61) 


(313) 






Pinterest 


8.48 


7.70 


8.15 


24.33 


75.67 


2.06-3.21 


4.27- 




(25) 


(20) 


(19) 


(64) 


(307) 




10.35 


Blogs, write/post 


6.92 


3.44 


4.41 


14.77 


85.23 


1.20-2.44 


1.45-5.93 




(26) 


(8) 


(15) 


(49) 


(323) 






Blogs, read/comment 


12.60 


4.87 


7.97 


25.44 


74.56 


1.58-3.06 


2.49-9.35 




(42) 


(14) 


(27) 


(83) 


(290) 






Gaming, network 


7.35 


10.25 


6.66 


24.26 


75.74 


1.70-3.01 


2.89-9.09 




(30) 


(26) 


(21) 


(77) 


(294) 






* Total responses varied by item from a low of 350 to a high of 391. 



Age was the most significant demographic differentiator, affecting all activities 



except group texting and blog posting. Use was highest among the youngest people 
surveyed (ages 1 8-29) and overall decreased as age increased. Women were significantly 
more likely than men to engage in text messaging (87.10% vs. 75.51%, p=0.015), in 

74 



voice calls (88.22% vs. 74.46%, p=0.005), and to be active on Facebook (60.50% vs. 
45.47%, p=0.040). Blacks (33.26%) were significantly more likely than whites (13.76%), 
Latinos (16.26%), or other racial/ethnic groups (8.59%) to participate in group chat 
rooms (p=0.022). English speakers were significantly more likely than Spanish speakers 
to send email (76.71% vs. 62.78%, p=0.040); to participate in group chat rooms (20.18% 
vs. 7.34%, p=0.019); to play games both alone (54.01% vs. 33.66%, p=0.013) and with 
others (28.72% vs. 12.86%, p=0.014), to post and view photos online (30.59% vs. 
14.59%, p=0.030); and to engage in social media activities such as Twitter (22.06% vs. 
8.96%, p=0.015), Pinterest (30.83% vs. 7.01%, p=0.003), and blog reading and 
commenting (29.73% vs. 13.45%, p=0.026). No other significant differences were 
observed. Additional detail is given in Table 19. 



Table 19: IT-Based Activities by Demographic 



N=405f 


Age 


Activity 


18-29 


30-39 


40-49 


50-59 


60-76 


Communication 








Email, send/receive (p<0.001*) 


86.01 


77.10 


82.86 


55.67 


44.33 


Text, send/receive (p<0.001*) 


98.58 


94.51 


92.68 


71.48 


42.77 


Voice calls, make/receive (p<0.001*) 


97.11 


92.15 


91.96 


67.59 


58.28 


Chat, video/Skype (p<0.001*) 


61.60 


45.65 


49.18 


21.15 


9.68 


Chat, direct text (p<0.001*) 


50.66 


32.32 


27.66 


13.16 


5.97 


Chat, group text (p<0.444) 


16.79 


13.41 


25.91 


12.29 


15.10 


Information 


News (p<0.001*) 


72.41 


70.50 


78.27 


44.10 


44.79 


Information (p<0.001 *) 


94.96 


83.73 


81.44 


49.38 


44.98 



75 



Table 19: IT-Based Activities by Demographic, continued 



N=405f 


Age 


Activity 


18-29 


30-39 


40-49 


50-59 


60-76 


Multimedia 












TV/movies (p<0.001*) 


61.60 


56.92 


65.28 


25.42 


27.95 


r Videos, watch (p<0.001 *) 


83.96 


68.89 


68.20 


38.29 


24.78 


Videos, make/post (p<0.001*) 


36.51 


32.22 


21.45 


6.23 


5.73 


Music, listen/play (p<0.001*) 


93.91 


76.93 


67.36 


49.95 


28.48 


Photos, online (p<0.001*) 


48.93 


23.68 


17.34 


12.85 


6.70 


Gaming, solo (p<0.001*) 


71.34 


38.06 


44.22 


38.16 


29.76 


Social media 


Facebook (p<0.001*) 


79.92 


61.74 


57.16 


36.84 


21.99 


Twitter (p<0.047*) 


25.60 


19.82 


20.91 


13.65 


2.56 


Pinterest (p<0.001*) 


42.94 


20.98 


27.95 


6.16 


5.49 


Blogs, write/post (p<0.074) 


20.10 


20.63 


12.82 


7.58 


4.72 


Blogs, read/comment (p<0.014*) 


36.00 


26.94 


28.73 


10.80 


13.80 


Gaming, network (p<0.001*) 


40.38 


17.41 


26.47 


11.50 


11.72 


* Significant p-value, 0.05 or less. 

fTotal responses varied by item from a low of 350 to a high of 391. 




Gender ! Race/Ethnicity j Lang 
















Communication 










Email, send/receive 


73.57 


69.90 


72.33 


65.57 


77.29 


82.67 


76.71* 


62.78* 


Text, send/receive 


87.10* 


75.51* 


85.18 


82.57 


84.73 


85.26 


84.57 


84.07 


Voice calls, make/receive 


88.22* 


74.46* 


86.30 


84.16 


79.86 


88.34 


85.28 


84.43 


Chat, video/Skype 


42.18 


41.85 


45.47 


34.35 


45.41 


41.89 


45.67 


32.75 


Chat, direct text 


30.03 


31.58 


28.57 


24.49 


38.25 


38.61 


30.71 


29.54 


Chat, group text 


14.93 


22.28 


13.76* 


16.26* 


33.26* 


8.59* 


20.18* 


7.34* 


Information 




News 


64.14 


65.93 


63.68 


61.30 


68.23 


70.03 


65.81 


61.50 


Information 


76.48 


75.01 


76.36 


69.63 


83.39 


81.76 


79.08 


69.25 


Multimedia 




TV/movies 


50.66 


51.85 


48.84 


44.19 


53.96 


67.39 


52.53 


47.05 


Videos, watch 


63.49 


56.86 


63.49 


56.06 


57.60 


73.38 


65.11 


53.98 


Videos, make/post 


23.27 


26.02 


22.91 


23.23 


26.22 


25.95 


24.79 


21.70 


Music, listen/play 


70.44 


66.72 


67.79 


67.46 


68.79 


80.74 


72.11 


63.44 


Photos, online 


25.32 


28.62 


29.21 


21.35 


23.37 


28.07 


30.59* 


14.59* 


Gaming, solo 


49.27 


45.30 


47.80 


45.64 


57.25 


45.41 


54.01* 


33.66* 


Social media 




Facebook 


60.50* 


45.47* 


55.83 


55.45 


57.62 


63.84 


59.48 


50.95 


Twitter 


18.18 


19.32 


15.19 


18.92 


29.26 


15.68 


22.06* 


8.96* 


Pinterest 


26.22 


17.75 


22.53 


18.21 


28.87 


36.92 


30.83* 


7.01* 


Blogs, write/post 


13.53 


19.13 


11.81 


17.46 


16.38 


17.51 


15.88 


11.86 


Blogs, read/comment 


26.07 


23.36 


25.28 


19.07 


30.20 


32.22 


29.73* 


13.45* 


Gaming, network 


24.53 


23.28 


24.85 


22.79 


24.85 


24.50 


28.72* 


12.86* 


* Significant p-value, 0.05 or less. 

fTotal responses varied by item from a low of 350 to a high of 391. 





Multiple regression analysis was conducted for each of these measures to examine 
the significance of general health status, mental health status, and unhealthy days in the 
last month, adjusting for demographic variables of age, gender, race/ethnicity, and 

76 



language. Playing games with other people was associated with mental health status, 
unhealthy days in the past month, language, and age. Talking to groups of people in chat 
rooms and watching TV or movies were also associated with unhealthy days in the past 
month, language, and age. Voice calls, watching videos, music, blog posting, reading and 
commenting, and the use of video chat were associated with general health status and 
age; video watching and music were also associated with other/unknown race/ethnicity, 
and video chat was also associated with Black race/ethnicity. Email was associated with 
White race/ethnicity, age, and language. Facebook use, Twitter, gaming, Pinterest, and 
online photos were associated with language and age. Age was associated with all 
remaining activities. No other significant associations were found. 
Health Information 

Table 20 presents utilization pattern results for health information users. Most 
health information users search for health information on someone else's behalf as well 
as their own (59.27%). Although most searches are infrequent, occurring every few 
weeks to once a month or less (68.45%), users place a high value on being able to look up 
health information, with 67.68% deeming it always or usually important. 



Table 20: Utilization Patterns among Health Information Users (n=231) 



Health information focus 


Responses 


Frequency 


Responses 


Std Err 


Variance 




(N=225) 


(weighted) 


(%) 


(%) 


(%) 


Self 


94 


23.74 


37.80 


4.21 


18.25 


Another person 


8 


1.85 


2.94 


1.23 


1.52 


Both self and other 


123 


37.22 


59.27 


4.27 


17.75 


Health information duration 


Responses 


Frequency 


Responses 


Std Err 


Variance 




(N=226) 


(weighted) 


(%) 


(%) 


(%) 


< 1 month 


17 


4.91 


7.78 


2.32 


5.39 


1-6 months 


23 


7.92 


12.56 


2.85 


8.13 


7-12 months 


11 


2.69 


4.26 


1.48 


2.20 


1-2 years 


34 


9.62 


15.25 


3.30 


10.91 


3-5 years 


45 


13.68 


21.68 


3.70 


13.72 


5-10 years 


58 


15.71 


24.90 


3.91 


15.28 


> 1 years 


38 


8.56 


13.56 


2.59 


6.71 



77 



Table 20: Utilization Patterns among Health Information Users (n=231) 



Health information frequency 


Responses 

(IN =ZZo ) 


Frequency 
(weighted) 


Responses 

(%) 


Std Err 

(%) 


Variance 

(%) 


Daily, multiple times 


1 A 

10 


2.64 


A 1 O 

4.18 


1 C 1 

1.51 


2.29 


Daily, once per day 


13 


r*t on 

2.89 


A CO 

4.58 


1.64 


2.70 


Weekly, 3-5 days per week 


25 


6.51 


10.32 


2.87 


8.24 


TI 7 1 1 1 1 J 1 

Weekly, 1-2 days per week 


27 


7.87 


12.47 


2.80 


7.82 


Monthly, every lew weeks 


49 


13.75 


21.79 


3.47 


12.01 


Monthly, once per month or less 


1 ni 

102 


on a a 

29.44 


46.66 


a on 

4.39 


1 c\ on 

19.30 


Health information value 


Responses 


Frequency 


Responses 


Std Err 


Variance 




(N=228) 


(weighted) 


(%) 


(%) 


(%) 


Always important 


104 


26.61 


41.70 


4.23 


17.88 


Usually important 


56 


16.58 


25.98 


3.79 


14.37 


Sometimes important 


54 


17.82 


27.93 


4.12 


16.97 


Rarely important 


11 


2.44 


3.83 


1.39 


1.93 


Not important 


3 


0.36 


0.57 


0.50 


0.25 



Multiple regression analysis was conducted for each of these measures to examine 



the significance of general health status, mental health status, and unhealthy days in the 
last month, adjusting for demographic variables of age, gender, race/ethnicity, and 
language. Frequency of health information searching was associated with general health 
status and unhealthy days in the past month. Gender, age, and language were associated 
with the recipient of health information searches (self, other, or both). No other 
significant associations were found. 

Table 21 describes utilization patterns for some commonly sought topics of health 
information. People most frequently looked for information about food, nutrition, or diet 
(78.54%); about exercise and physical activity (71.93%); about diseases and illnesses that 
either they (71.29%) or someone else (70.68%) had; and about medications they take 
(66.24%). People were least likely to look up information about lab test results (25.99% 
self, 15.94% other) and clinic visit notes (25.00% self, 13.43% other). 



78 



Table 21: Health Information Conl 


ent (n=237*) 


Health information topic 


Responses 

(N) 


Frequency 
(weighted) 


Responses 

(%) 


Std Err 

(%) 


Variance 

(%) 


Disease, self 


182 


45.60 


71.29 


3.98 


15.84 


Disease, another's 


164 


44.49 


70.68 


3.98 


15.83 


Surgery, self 


85 


19.84 


32.15 


3.96 


15.71 


Surgery, another's 


101 


25.62 


41.61 


4.27 


18.22 


Feelings/ symptoms 


140 


39.62 


62.98 


4.23 


17.88 


Medicines, self 


159 


42.16 


L 66 - 24 


4.02 


16.15 


Medicines, another's 


112 


29.70 


47.60 


4.38 


19.21 


Health insurance 


112 


30.78 


49.67 


4.41 


19.49 


Doctors/health care providers 


125 


34.72 


55.60 


4.38 


19.17 


Lab test results, self 


67 


16.19 


25.99 


3.76 


14.12 


Lab test results, another's 


38 


9.69 


15.94 


3.21 


10.28 


Clinic visit notes, self 


56 


15.22 


25.00 


3.98 


15.81 


Clinic visit notes, another's 


32 


8.09 


13.43 


3.06 


9.37 


Exercise/physical activity 


158 


44.13 


71.93 


3.94 


15.52 


Food, nutrition, or diet 


183 


49.57 


78.54 


3.52 


12.38 


Birth control/family planning 


69 


26.80 


43.78 


4.51 


20.37 


Behavior change (eg, 
alcohol/tobacco cessation) 


92 


21.66 


35.15 


4.09 


16.72 


Health topics in the news 


134 


36.43 


60.72 


4.45 


19.85 


* Total responses varied by item from a low of 215 to a high of 232. 



Multiple regression analyses were conducted for each of these measures to 



examine the significance of general health status, mental health status, and unhealthy 
days in the last month, adjusting for demographic variables of age, gender, race/ethnicity, 
and language. Unhealthy days in the past month together with age and other 
race/ethnicity were associated with one's own disease. General health status was 
associated together with language for one's own upcoming surgery, for one's own lab test 
results, and for behavorial change interest. Language was associated with medicine use 
both by self and others and with looking up information about health care providers. Age 
and Latino race/ethnicity were associated with looking for information about birth control 
and family planning. No other significant associations were found. 



79 



Health Communication 

Table 22 presents utilization patterns for health communicators. Half of those who 
engage in health communications have been talking with others about health or health 
care for less than 3 years (50.78%). The majority of those who talk with others about 
health and health care do so relatively infrequently (77.19%, monthly or less) but believe 
such communications to be usually or always important (64.90%). 



Table 22: Utilization Patterns among Health Communical 


tors (n=192) 


Health communication 


Responses 


Frequency 


Responses 


Std Err 


Variance 




(N=192) 


(weighted) 


(%) 


(%) 


(%) 


< 1 month 


26 


7.80 


15.74 


3.58 


12.78 


1-6 months 


20 


6.43 


12.98 


3.35 


11.21 


7-12 months 


13 


3.59 


7.25 


2.53 


6.40 


1-2 years 


28 


7.34 


14.81 


3.40 


11.57 


3-5 years 


38 


11.07 


22.33 


4.04 


16.29 


5-10 years 


22 


4.36 


8.79 


2.15 


4.63 


> 1 years 


45 


8.97 


18.10 


3.23 


10.41 


Health communication 


Responses 


Frequency 


Responses 


Std Err 


Variance 














Daily, multiple times 


11 


2.15 


4.42 


1.78 


3.18 


Daily, once per day 


8 


1.86 


3.83 


1.63 


2.64 


Weekly, 3-5 days per week 


15 


2.94 


6.04 


2.09 


4.37 


Weekly, 1-2 days per week 


18 


4.15 


8.53 


2.71 


7.32 


Monthly, every few weeks 


59 


17.56 


36.11 


4.52 


20.43 


Monthly, once per month or less 


78 


19.97 


41.08 


4.63 


21.42 


Health communication value 


Responses 
(N=192) 


Frequency 
(weighted) 


Responses 

(%) 


Std Err 

(%) 


Variance 

(%) 


Always important 


81 


20.51 


41.39 


4.57 


20.92 


Usually important 


42 


11.65 


23.51 


3.94 


15.55 


Sometimes important 


56 


14.91 


30.09 


4.38 


19.17 


Rarely important 


10 


2.41 


4.86 


2.21 


4.90 


Not important 


3 


0.07 


0.15 


0.09 


0.01 



Multiple regression analyses were conducted for each of these measures to 



examine the significance of general health status, mental health status, and unhealthy 
days in the last month, adjusting for demographic variables of age, gender, race/ethnicity, 
and language. No health status variables were found to be significantly associated. 
Language was associated with health communication duration, and health communication 



frequency was associated with black race/ethnicity. No other significant associations 
were found. 

Table 23 describes who people use IT to talk with about health and health care. 
The majority of people use IT to communicate with their family (88.32%), friends 
(74.54%), and their health care providers (71.42%). Very few communicate with others 
online about health and health care (7.24%). 



Table 23: Health Communication Contacts (n=2 


10*) 


Health communication contact 


Responses 


Frequency 


Responses 


Std Err 


Variance 




(N) 


(weighted) 


(%) 


(%) 


(%) 


Health care provider 


142 


36.57 


71.42 


4.00 


16.01 


Family 


178 


46.15 


88.32 


2.88 


8.32 


Friends 


148 


37.43 


74.54 


3.93 


15.45 


Online acquaintances 


15 


3.44 


7.24 


2.30 


5.29 


Co-workers 


64 


16.78 


34.74 


4.60 


21.17 


Students/classmates 


31 


10.18 


21.33 


4.14 


17.14 


Neighbors/community 


53 


14.58 


29.65 


4.49 


20.17 


members 












Religious leaders 


29 


5.85 


12.49 


3.00 


9.00 


* Total responses varied by item from a low of 178 to a high of 202. 



Multiple regression analyses were conducted for each of these measures to 
examine the significance of general health status, mental health status, and unhealthy 
days in the past month, adjusting for demographic variables of age, gender, 
race/ethnicity, and language. Talking about health topics with neighbors and community 
members was associated with general health status and unhealthy days in the past month. 
Talking about health topics with (fellow) students was associated with age; talking about 
health topics with family members was associated with age and language. Talking about 
health topics with online acquaintainces was associated with language and Latino and 
other/unknown race/ethnicity. Talking about health topics with religious leaders was 



81 



associated with black and other/unknown race/ethnicity. No other significant associations 
were found. 

Other Technology Users 

Table 24 presents utilization for other types of common technology with 
information delivery capacity. Multimedia entertainment technology such as DVD and/or 
Blu-ray players (74.51%), cable TV with "on-demand" features (73.55%), and portable 
digital music players (5 1 .02%) were the most frequently used. Just under half of 
respondents reported using health-specific technology devices (44.96%). Streaming 
media boxes (12.05%) and e-book readers (16.97%) were the least used. 



Table 24: Other Technology Utilization (n=403*) 



Technology type 


Responses 

(N) 


Frequency 
(weighted) 


Responses 

(%) 


Std Err 

(%) 


Variance 

(%) 


Health-specific technology (eg, 


215 


42.59 


44.96 


3.34 


11.16 


glucometer, blood pressure cuff) 












E-book reader 


62 


15.54 


16.97 


2.61 


6.79 


MP3/music player 


164 


47.26 


51.02 


3.38 


11.42 


Game console 


142 


39.82 


43.49 


3.45 


11.92 


DVD/Blu-ray player 


277 


69.46 


74.51 


2.95 


8.72 


Cable TV (with "on-demand") 


282 


68.69 


73.55 


3.01 


9.04 


Streaming media device 


51 


10.95 


12.05 


2.23 


4.97 


Internet-enabled TV 


124 


33.05 


36.48 


3.35 


11.22 


* Total responses varied by item from a low of 372 to a high of 395. 



Health-specific device use increased in association with age, with the highest use 



found in the highest age bracket (ages 60-76, 62.69%, p=0.036). Age was also strongly 
associated (p<0.001) with game console use, with the youngest age bracket having the 
highest use (65.37%) and utilization decreasing as age increased. English speakers were 
significantly more likely than Spanish speakers to use all types of examined technology 
except for streaming media boxes. Whites were more likely than all other 
races/ethnicities to use DVD/Blu-ray players and cable with on-demand service. Women 



82 



were slightly more likely than men to use e-book readers (17.13% vs. 16.44%, p<0.013). 
Additional detail is shown in Table 25. 

Table 25: Other Technology Users, Utilization by Demographic 





Health Device 


eBook 


MP3 


Game Console 


N=403f 


Yes 


No 

% 


Yes 

% 


No 

% 


Yes 

% 


No 

% 


Yes 

% 


No 

% 


Aee 


p=0.036* 


p<0.343 


p<0.001* 


p<0.001* 


18-29 


36.16 


63.84 


13.38 


86.62 


77.11 


22.89 


65.37 


34.63 




34.94 


65.06 


15.52 


84.48 


63.81 


36.19 


57.27 


42.73 


4U— 49 


50.50 


49.50 


27.35 


72.35 


45.19 


54.81 


34.87 


65.13 


50-59 


52.14 


47.86 


18.76 


81.24 


24.18 


75.82 


20.96 


79.04 


60-76 


62.69 


37.31 


11.43 


88.57 


15.12 


84.87 


11.84 


88.16 


Gender 


p=0.079 


p<0.013* 


p=0.241 


p=0.860 


L Female 


45.44 


54.56 


17.13 


82.87 


53.01 


46.99 


43.19 


56.81 


Male 


43.44 


56.56 


16.44 


83.56 


44.50 


55.50 


44.49 


55.51 


Race 


p=0.603 


p=0.315 


p=0.006* 


p=0.530 


White 


43.87 


56.13 


19.15 


80.95 


54.59 


45.41 


45.94 


54.06 


Latino 


39.77 


60.23 


9.54 


90.46 


34.17 


65.83 


35.99 


64.01 


Black 


50.71 


49.29 


23.55 


76.45 


56.37 


43.63 


50.84 


49.16 


Other 


52.65 


47.35 


18.51 


81.49 


68.29 


31.71 


42.77 


57.23 


Language 


p=0.001* 


p=0.001* 


p=0.004* 


p=0.007* 


L English 


51.99 


48.01 


21.91 


78.09 


57.44 


42.56 


49.29 


50.71 


Spanish 


27.74 


72.26 


4.80 


95.20 


35.17 


64.83 


28.86 


71.14 


^Significant p-value, 0.05 or less. 

f Responses vary by item from a low of 379 to a high of 403. 




DVD/ 
Blu-ray 


Cable w/ 
On-Demand 


Streaming 
Media Box 


Internet 

TV 


N=403f 


Yes No 

% % 


Yes 

% 


No 

% 


Yes 

% 


No 

% 


Yes No 

% % 


Age 


p=0.031* 


p=0.390 




p=0.0L 


? 


p=0.044 


18-29 


80.23 


19.77 


79.36 


20.63 


14.70 


85.30 


48.54 


51.46 


30-39 


81.40 


18.60 


77.79 


22.21 


18.82 


81.18 


39.57 


60.43 


40-49 


80.23 


19.77 


64.02 


35.98 


7.81 


92.19 


33.28 


66.72 


50-59 


59.24 


40.76 


70.18 


29.82 


10.18 


89.82 


24.54 


75.46 


60-76 


61.40 


38.60 


70.33 


29.67 


3.24 


96.76 


23.23 


76.77 


Gender 


p=0.969 


p=0.057 


p=0.837 


p=0.519 


Female 


74.57 


25.43 


72.74 


27.26 


11.82 


88.18 


35.40 


64.60 


Male 


74.33 


25.67 


76.33 


23.67 


12.80 


87.20 


40.01 


59.99 


Race 


p<0.001* 


p=0.001* 


p=0.954 


p=0.231 


White 


82.40 


17.60 


78.38 


21.62 


10.72 


89.28 


37.06 


62.94 


Latino 


56.58 


43.42 


58.47 


41.53 


12.23 


87.77 


29.96 


73.04 


Black 


86.84 


13.16 


93.34 


6.66 


14.27 


85.73 


41.14 


58.86 


Other 


70.59 


29.41 


68.49 


31.51 


13.50 


86.50 


48.54 


51.46 


Language 


p<0.001* 


p<0.001* 


p=0.161 


p=0.011* 


English 


81.35 


18.65 


81.48 


18.52 


14.02 


85.98 


41.93 


58.07 


Spanish 


57.97 


42.03 


55.25 


44.75 


7.16 


92.84 


22.72 


72.28 


^Significant p-value, 0.05 or less. 

f Responses vary by item from a low of 379 to a high of 403. 



83 



Technology Nonusers 

Barriers and facilitators to IT and HIT use were explored with respondents who 
reported not using computers, cell phones, or the internet, or not engaging in health 
information seeking or health communications. Detailed results are shown in Table 26. 
Knowledge was the most frequent barrier reported for computer (62.69%) and Internet 
(61.96%) use, although it was much less of a factor for cell phone use (15.68%). Access 
was the second most frequent barrier reported for computer and Internet use, and the 
highest barrier to cell phone use (43.76%; 43.27%, Internet; 32.61%, computer). Cost 
was the second-highest barrier to cell phone use (31.09%) and the third-highest barrier 
for computer and Internet use (25.64%, 31.92%). The highest barrier to the use of health 
information and to health communication was identified as preference for personal 
interaction with one's health care provider (49.75%, health information; 50.87%, health 
communication), with knowledge being identified as the second most frequent (39.24%, 
health information; 26.03%, health communication). 

Definite interest by nonusers in engaging with IT and HIT was reported across all 
categories of nonuse: computer (49.57%), cell phone (34.73%), Internet (51.13%), health 
information (45.61%), and health communication (27.99%). Disinterest was higher than 
definite interest for health communication (40.74%) and cell phone use (39.95%). Interest 
modified by knowledge, access, and cost was reported for all 5 categories of nonuse. Half 
the population identified education as a facilitator to computer (50.51%) and Internet use 
(50.1 1%). Reduced pricing (cost) was the most frequent facilitator to cell phone use, 
identified by half of cell phone users (50.23%), and the second most frequent for 
computer (33.90%) and Internet use (45.24%). 

84 



Table 26: IT and HIT Barriers and Facilitators 





Computer 


Cell 


Internet 


H. Info 


H.Comm 


SE, % 


Var, % 




%(n) 


%(n) 


%(n) 


%(n) 


%(n) 






















Barrier 








Access 


32.61 


43.76 


43.27 


N/A 


N/A 


5.24- 


27.46- 




(53) 


(21) 


(40) 






10.95 


119.83 


Knowledge 


62.69 


15.39 


61.96 


39.24 


26.03 


4.23- 


17.87- 




(95) 


(8) 


(82) 


(70) 


(57) 


7.71 


59.37 


Need 


20.10 


15.68 


15.31 


14.26 


18.28 


3.67- 


13.46- 




(25) 


(10) 


(17) 


(19) 


(29) 


7.37 


54.32 


Interest 


20.52 


26.69 


18.41 


17.96 


23.15 


4.28- 


18.33- 




(27) 


(11) 


(23) 


(24) 


(30) 


9.53 


90.83 


Difficulty 


15.10 


16.81 


13.75 


8.41 


11.96 


3.05- 


9.31- 




(21) 


(7) 


(20) 


(16) 


(20) 


7.82 


61.17 


Cost 


25.64 


31.09 


31.92 


N/A 


N/A 


4.82- 


23.24- 




(40) 


(14) 


(33) 






10.19 


104.02 


Value 


8.44 


8.10 


6.44 


N/A 


N/A 


2.92- 


8.52- 




(9) 


(3) 


(8) 






5.53 


30.63 


Trust 


N/A 


N/A 


N/A 


7.51 


N/A 


2.87 


8.24 










(8) 








Health literacy 


N/A 


N/A 


N/A 


8.31 


7.21 


2.60- 


6.78-8.53 










(11) 


(10) 


2.92 




Privacy 


N/A 


N/A 


N/A 


15.84 


16.40 


3.73- 


13.98- 










(26) 


(30) 


4.10 


16.83 


Personal interaction 


N/A 


N/A 


N/A 


49.75 


50.87 


5.29- 


27.99- 










(84) 


(93) 


5.43 


29.51 


Isolation 


N/A 


N/A 


N/A 


N/A 


19.28 


4.03 


16.23 












(31) 






Facilitator 


Education 


50.51 


19.37 


50.11 


N/A 


N/A 


6.01- 


36.14- 




(73) 


(8) 


(59) 






8.87 


78.74 


Pricing 


33.90 


50.23 


45.24 


N/A 


N/A 


5.30- 


28.09- 




(58) 


(25) 


(44) 






11.61 


134.71 


Fluency 


22.57 


8.41 


32.91 


N/A 


N/A 


4.68- 


21.88- 




(32) 


(4) 


(29) 






6.31 


42.54 


Unknown 


31.49 


32.47 


29.41 


N/A 


N/A 


5.89- 


34.74- 




(32) 


(9) 


(29) 






10.95 


119.91 


Interest 


Definite 


49.57 


34.73 


51.13 


45.61 


27.99 


4.55- 


20.75- 




(73) 


(15) 


(57) 


(75) 


(59) 


10.66 


113.61 


Moderated, access 


32.49 


30.74 


21.66 


20.39 


10.48 


2.77- 


7.65- 




(47) 


(15) 


(28) 


(38) 


(25) 


10.39 


107.86 


Moderated, cost 


23.68 


30.07 


25.34 


20.66 


16.31 


3.87- 


14.99- 




(38) 


(16) 


(27) 


(35) 


(29) 


8.80 


77.45 


Moderated, 


45.88 


19.41 


35.38 


26.53 


14.95 


3.43- 


11.78- 


knowledge 


(61) 


(8) 


(39) 


(42) 


(28) 


8.31 


69.04 


Moderated, security 


N/A 


N/A 


N/A 


19.17 


19.79 


4.17- 


17.36- 










(30) 


(36) 


4.21 


17.69 


Uncertain 


12.42 


6.43 


5.40 


13.13 


15.73 


2.73- 


7.44- 




(13) 


(3) 


(8) 


(20) 


(22) 


5.62 


31.55 


None 


18.01 


39.95 


36.39 


30.21 


40.74 


4.17- 


17.36- 




(26) 


(16) 


(35) 


(43) 


(67) 


10.53 


110.87 


* Responses varied by item: 135 - 142 (computer); 38 -45 (cell); 108 -116 (internet); 159 -161 (health info); 176- 


183 (health commn). 

















85 



Demographic differences among nonusers were examined by age, gender, 
race/ethnicity, and language. Race was strongly significant across all categories of 
nonuse (p<0.001). Latinos were most likely to be computer nonusers (41.99%) and 
Internet nonusers (42.16%), while whites were most likely to be cell phone nonusers 
(45.83%), health information nonusers (42.63%), and health communication nonusers 
(46.45%). Women were significantly more likely than men to be nonusers in all 
categories. People ages 50-59 were most likely to be cell phone nonusers (31.51%, 
p=0.040) and health information nonusers (26.80%, p=0.047), while people ages 18-29 
were most likely to be health communication nonusers (31.98%, p<0.001). English 
speakers were more likely than Spanish speakers to look up health information (61.31% 
vs. 38.69%, p=0.034) and engage in health communication (67.86% vs. 32.14%, 
p<0.001). Additional detail is shown in Table 27. 



Table 27: IT and HIT Nonusers by Demographic 





Computer 


Cell Phone 


Internet 


Health Info 


Health Comm 




% (n=149) 


% (n=48) 


% 0i=125) 


% (n=179) 


% (n=203) 


Age 


p=0.388 


p=0.040* 


p=0.121 


p=0.047* 


p<0.001* 


18-29 


13.68 


1.06 


11.96 


15.02 


31.98 


30-39 


20.18 


7.58 


15.59 


18.27 


15.68 


40-49 


16.04 


10.98 


11.45 


17.65 


12.35 


50-59 


20.57 


31.51 


28.56 


26.80 


20.34 


60-69 


20.71 


22.91 


21.32 


14.57 


13.23 


70-76 


8.81 


25.96 


11.11 


7.69 


6.41 


Gender 


p<0.001* 


p=0.018* 


p<0.001* 


p=0.002* 


p<0.001* 


Female 


74.58 


72.01 


14.13 


65.24 


70.50 


Male 


25.41 


27.99 


25.27 


34.76 


29.50 


Race/Ethnicity 


p<0.001* 


p<0.001* 


p<0.001* 


p<0.001* 


p<0.001* 


White 


37.19 


45.83 


39.20 


42.63 


46.45 


Hispanic/Latino 


41.99 


44.41 


42.16 


37.58 


31.72 


Black 


9.60 


0.67 


9.72 


15.47 


12.82 


Other/Unknown 


11.22 


9.09 


8.92 


4.33 


9.01 


Language 


p=0.097 


p=0.578 


p=0.701 


p=0.034* 


p<0.001* 


English 


49.81 


55.64 


52.32 


61.31 


67.86 


Spanish 


50.19 


44.36 


47.68 


38.69 


32.14 


^Significant p-value, 0.05 or less. 



86 



Opinions about Technology 

Both technology nonusers and technology users expressed significant interest in 
HIT and reported perceived value of IT both in general and for HIT purposes. Many 
identified specific HIT solutions of interest and offered suggestions for the health system, 
such as requesting the ability to make appointments online, by email, or by text; to 
communicate with providers by email or text; and to access their information through 
patient portals or similar solutions. Barriers to IT and HIT use were noted repeatedly, 
particularly in terms of cost to obtain, use, and repair cell phones and computers; in terms 
of knowledge and the need for training to better make use of existing available 
technology; and in terms of access, specifically with regard to DH systems not supporting 
either IT-based access to patients' health information or the ability to use IT to 
communicate with health care providers. Respondents also made frequent reference to 
their perceptions of advances in IT as an overall years-long trend that they expected to 
continue. Some expressed concerns about IT and/or HIT, including concern about the 
trustworthiness of health information found online and concern about the isolating or 
distancing effects of technology, along with fears that IT solutions might be used to 
replace face-to-face or personal contact with health care providers. 

Health Status and Information Technology Use 

Multiple regression analyses were used to assess the impact of health information 
user status and health communications user status on CDC HRQOL measures of general 
health status (good, very good, or excellent self-rated health versus fair or poor self-rated 
health), mental health status as assessed by the presence or absence of frequent mental 
distress, and the number of unhealthy days reported in the past 30 days, while controlling 

87 



for demographic variables of age, gender, race, and primary language. Analysis was 
conducted at the population level and within each risk tier subgroup. Model fit was was 
assessed by convergence criterion satisfaction, the difference between Akaike 
Information Criterion, Schwarz Criterion, and negative two times the log-likelihood 
values for intercept-only and intercept-with-covariates and by examining global null 
hypothesis testing results (154). 
Population Level 

General health status of good or better was significantly associated with health 
information use (p=0.001), with age, with black and other/unknown race/ethnicity, and 
with language in the presence of black and other/unknown race/ethnicity. General health 
status was not associated with health communication. 

Mental health status was associated with age and language, but not with health 
information use or health communication. Unhealthy days in the past month was 
associated with age, but not with health information use or health communication. 
Tier 1 Level 

General health status of good or better was associated with language. Mental 
health status was associated with gender and other/unknown race/ethnicity, and number 
of unhealthy days in the past month was associated with gender. None of the 3 were 
associated with health information use or health communication. 
Tier 2 Level 

General health status of good or better was associated with health information use 
(p=0.025), age, gender, and other/unknown race/ethnicity. Mental health status was 
associated with age and language. Number of unhealthy days in the past month was 

88 



associated with age and white race/ethnicity. Neither mental health status nor unhealthy 
days in the past month were associated with health information use, and none of the 3 
health measures were associated with health communication. 
Tier 3 Level 

No significant associations were found between general health status, mental 
health status, or unhealthy days in the past month and either health measures or 
demographic measures. Examination of the number of observations in this tier (141 read; 
133 used) and the associated post-stratification weights used in analysis (sum of 
4.569542 read; sum of 4.327224 used) led to consideration of the possibility that there 
was insufficient power to detect measurable results in this stratum. A secondary 
examination through unweighted analysis was conducted, on the basis that tier weighting 
was not applicable in a single-stratum analysis and that regression techniques would 
adjust for potential disproportionate impact of age. The findings from the unweighted 
analysis were concordant with the weighted analysis, confirming the results. 

Diffusion of Innovations: Technology Diffusion Assessment 

Computers, cell phones, and the Internet were selected as examples of IT 
innovations that have achieved the "plateau of productivity" because of the length of time 
between their inception and widespread use. A technology can be deemed to have entered 
the plateau once 20-30% of potential users have adopted it (155). 

A combination of user versus nonuser status and duration of technology use 
reported for each of these 3 technologies were used to categorize users into 5 groups in 
potential alignment with the 5 categories of innovation adopters described in Dol theory. 
Group distribution in the surveyed population was then compared to a reference 

89 



population of 200 model users classified into adopter categories in strict alignment with 
the Dol theoretical population distribution (Figure 3). 

Figures 9, 10, and 1 1 show duration of technology use (ie, adoption) for 
computers, cell phones, and the Internet in the surveyed population. 



40- 



30- 



20 



lO- 



II - 



1 1 1 r r r r 

l:<lmonth 2:l-Gmos i: 7-12 man 4: l-2yrs 5: 3-5 yrs 6:5-10yrs 7:10+yrs 



Figure 9: Computer Adoption - Duration of Use 



40- 



10- 



g 20 



10- 



o- 



I ll ll I 



1: < 1 month 2: 1-6 mos 3: 7-12 mog 4: 1-2 yrs 5: 3-5 jts 6: 5-10 yrs 7: 10+ yrs 

Figure 10: Cell Phone Adoption - Duration of Use 



90 



id 




Figure 11: Internet Adoption - Duration of Use 



Surveyed users were classified as potential innovators if they had adopted all 3 
technologies and were using the newest or most advanced technology in each class (tablet 
computers, smartphones, and broadband internet access). Users were classified as 
potential early adopters if they had not already been classified as potential innovators, if 
they had adopted all 3 technologies, and if they had been using each technology for more 
than 10 years. Users were classified as potential laggards if they had adopted none of the 
three technologies or if they had not adopted both computers and the Internet. Users were 
classified as potential early majority if they had not already been classified as potential 
innovators or early adopters and if they had been using one or more of the 3 technologies 
for between 5 and 10 years. Users were classified as potential late majority if they had 
not already been classified and they had been using 1 or more of the 3 technologies for up 



91 



to 5 years. Table 28 shows the resulting classification in comparison to the reference 
population model. 

Table 28: Technology Diffusion Classification, Surveyed Population 





Surveyed 
N=415 


Surveyed 
% (wgt) 


Model 
N=200 


Model 

% 


Adopter Category 


(p=0.754) 


Innovator (1) 


13 


3.40 


5 


2.50 


Early Adopter (2) 


49 


11.84 


27 


13.50 


Early Majority (3) 


130 


36.11 


68 


34.00 


Late Majority (4) 


105 


28.16 


68 


34.00 


Laggards (5) 


118 


20.49 


32 


16.00 



No significant difference between populations was found (p=0.754). Figure 12 
shows the distribution for both reference (group 1) and surveyed (group 2) populations. 



diff group 



= 1 



30 
25 

20' 

:o 

1 o 

v 
u 

30 
25 
20 

10 

5 




diff group 



Figure 12: Model and Surveyed Population Distributions 



92 



CHAPTER VI 
DISCUSSION 

This study is believed to be the first to conduct a detailed examination of IT 
utilization patterns and characteristics both in general and when used for health 
informatics purposes among the vulnerable populations traditionally served by the health 
care safety net, as well as the first to propose and examine the applicability of Dol 
theoretical principles to a single socioeconomic stratum. The results of this study are 
anticipated to be useful for developing appropriate HIT-based, patient-centered health 
service delivery approaches and potentially informative for HIT policy and infrastructure 
development. 

Limitations of this study include the assumption for study purposes that the 
population surveyed for this study accurately represents the homeless or other groups 
without a mailing address or telephone number as fixed forms of contact, given that 
survey outreach was conducted by postal mailing. Although the impact of these 
limitations is expected to have been minimal, given the low number of people excluded 
from the sampling frame under these criteria, future studies might address this concern by 
dedicating resources to outreach conducted at the point of care for these individuals. It is 
also noteworthy that fewer responses were received from people between the ages of 1 8 
and 29 than in older age brackets, especially considering that technology adoption and 
use is significantly higher in younger populations. Consideration should be given to the 
potential for improving survey response among technology users by supporting targeted 
online methods for survey invitation and participation in comparison to paper-based 
versions. Consideration should also be given to the possibility of technology nonuser 

93 



underrepresentation among respondents. While post-stratification weighting was used to 
adjust for age differences and nonresponse bias was deemed to have low probability 
given the close matching of eligible and respondent populations on demographics of 
gender, race/ethnicity, and language, future studies might incorporate alternate ways to 
reach and engage these specific groups in survey response. 

The findings from this study further support the IOM's assertion that information 
technology can be used to improve consumer health (1), and also extend the applicability 
of that recommendation to specifically include priority populations among those who can 
benefit from consumer health informatics solutions. Given the observed effectiveness of 
HIT solutions for health care in all four domains of the Chronic Care Model (73, 75), this 
is of particular importance considering that the higher prevalence and greater burden of 
chronic disease previously reported among priority populations (65, 67) was observed in 
the study population as well. Two-thirds (65.2%) of the study population were stratified 
into higher-risk tiers based on current chronic disease status and elevated chronic illness 
(CDPS) risk score, compared to a reported prevalence of chronic disease in half the U.S. 
population overall (66). Self-reported health status was poorer in the study population as 
well, with 26.4% reporting fair or poor general health, compared to 15.9% nationwide; 
18% reporting frequent mental distress, compared to 10% nationwide; and a mean of 10 
unhealthy days per month compared to 6 unhealthy days per month nationwide (156). 

The first specific aim of this study was to assess and describe current methods and 
patterns of IT utilization in general and for health information access and engagement in 
health communications among adult patients who receive care in an urban safety net 
setting. Specific attention was given to how patterns of general utilization, utilization for 

94 



health information access, and utilization for health communication might differ by 
demographic subgroup or health status, as well as to barriers and facilitators encountered 
by patients with regard to IT and HIT use and to opinions held by patients about IT and 
HIT. It was anticipated that safety net patients with chronic diseases would have greater 
interest in, engagement with and utilization of IT for health information access and health 
communications than would those without chronic illness. 

For technologies that have reached the point of widespread, relatively inexpensive 
access, utilization in the study population was similar to utilization reported both 
nationwide and in similar populations. Internet use among the surveyed population was 
reported by 74%, comparable to previously-reported nationwide use (74%) and to use 
assessed separately among medically underserved groups (72%) and primary care 
patients (78%) (157-160) .The increasing availability of lower-cost mobile technology 
appears to contribute to higher adoption and utilization in the study population than 
nationally in some categories. For example, 93% of those surveyed use cell phones, 
compared to 87% of adults nationally, and 51% of those surveyed reported smartphone 
use, compared to 45% of adults nationally (125). This finding is similar to findings of 
mobile "leapfrogging" occurring among priority populations (161, 162), referring to the 
generally-higher adoption of mobile technology in preference to older, more traditional 
methods (eg, personal computers). In addition to its observance here, higher cell phone 
utilization among health care consumers has been reported among those with chronic 
illness (163) and those enrolled in substance abuse treatment (91%; (164)). 

Evidence of the beneficial impact of IT and HIT on health was observed in the 
study population as well. Computer ownership and computer use-value were found to be 

95 



associated with general and mental health status, cell phone use was found to be 
associated with unhealthy days in the past month, duration of Internet use was found to 
be associated with general health status, and general health status of good or better was 
significantly associated with health information use (p=0.001). These findings not only 
uphold the IOM's claim, but also give credence to the importance of the fourth goal of 
the ONC's Federal Health Strategic Plan under the HITECH Act, which is to empower 
individuals with HIT in order to improve both the individuals' health and the health care 
system overall (34) - a goal which implicitly acknowledges the relevance of Foucault's 
power-knowledge dyad. 

In addition to their micro-level, applied Foucaldian significance, examining these 
findings in the macro-level context of the previously-described Marxist healthcare 
construct shown in Figure 2, which considers the societal superstructure of health care in 
the U.S. as a capitalist system in which priority population patients are representative of 
the worker class, gives additional meaning to the observed associations between IT, HIT, 
and health status. These associations can be recognized as illustrative of the patient- 
worker having overcome alienation from access to the commodity of health information 
through achieving ownership of the means of production in the form of technological 
capacity. Furthermore, the fact that the technological means are selected by the patient- 
workers themselves according to their own preferences rather than imposed upon them by 
ruling-class capitalist entrepreneurs allows for emergence from beneath the dominant 
pressure of Gramsci's cultural hegemony. The problem of the patient-workers' 
spontaneous consent to their own exploitation is solved through the patient-workers' 



96 



adoption and use of technological means that have already undergone dissemination from 
the elite to the masses and been accepted as such. 

Additional examination of detailed study findings in accordance with these 
theoretical contexts cautions against pursuing a strictly structuralist, one-size-fits-all 
infrastructure-based approach to HIT solutions and supports the importance of addressing 
issues of cultural hegemony through patient-centered methods. The predominance of 
mobile methods of IT use - notebook (68%) over desktop (56%) computers as well as the 
93% prevalence of cell phones - suggests that solutions designed to take advantage of 
mobile capacities and limitations will be best-suited for the study population. Smartphone 
use among half (51%) the cell phone-using population indicates that health app 
development may be a worthwhile investment. Significant differences observed among 
IT activity patterns, such as the preference for text messaging (84%) over email (73%), 
the preference for video chat (42%) over text chat (30%), and the fact that over half the 
study population engages in video-watching (62%) and is on Facebook (57%) offer 
possibilities for tailoring health messaging to appropriate communication channels. 

The significant age-based differences in prevalence of IT use suggest that 
technology-focused solutions may be more quickly accepted among younger patients; at 
the same time, the fact that half of patients 60 and above report technology use indicates 
that health care providers may find value in HIT solutions that take into consideration the 
needs of geriatric populations as well. This finding is also supported by studies indicating 
disparity in older users' technology use (165-167) as compared to that of younger users. 
The spike of higher health communication use in the 30s and 40s (66% and 64% 
respectively) implies the existence of a period during which patients may have greater 

97 



interest in engaging in HIT activities in this domain, and thus a potential timeframe for 
intervention. This finding is supported by similar observations from the Health 
Information National Trends Survey, in which health information exchange was found to 
be more highly valued by users age 45 to 54 than by younger users (168). 

Significant differences observed by race/ethnicity clearly indicate that HIT 
solutions need to be culturally sensitive in order to be successful. For example, black 
patients had the highest IT utilization of all racial/ethnic groups, across all types of IT: 
computers (82%), cell phones (99%), and the Internet (85%), suggesting that a variety of 
HIT solutions for health concerns that more strongly affect black patients and 
communities may be well-accepted, where solutions targeted toward Hispanic/Latino 
patients and communities may need to be confined to more widely-adopted channels. 
Evidence indicates that culturally sensitive approaches toward HIT solutions have been 
found useful; for example, findings from the Men's Prostate Awareness Church Training 
Project, targeted at church-based health promotion for black men, indicate potential for 
incorporating HIT support through these channels (169), while user-centric design studies 
among black youth identified the culture-specific need for inclusion of trust components 
in their informatics solutions (170). In addition, Spanish speakers were significantly less 
likely than English speakers to engage in health information seeking and in text- 
dependent activies such as email, social media, and blog reading and commenting. This 
suggests that current Spanish-language IT-based print resources may be limited and that 
visual or audio HIT solutions may be more appropriate for this group, especially when 
considered in light of findings that indicate health literacy challenges are encountered 
when seeking health information online (171, 172). 

98 



The second aim of this study was to compare the health status of IT users and IT 
non-users among adult patients with chronic disease who receive health care in an urban 
safety net setting. Adult patients with chronic disease in this setting who use IT to access 
health information and engage in health communications were predicted to have better 
health status than adult patients with chronic disease and who were non-IT users. 
Findings at the population level indicated a significant, positive association between 
health information seeking and general health status, as did findings within Tier 2, where 
chronic disease was present but better-controlled than among patients in Tier 3. No 
significant associations with health information were seen in other tiers, nor were 
significant associations with health communications found to exist. 

Where health information seeking is concerned, findings about popular topics 
among the study population offer insight into potential areas of interest where health care 
providers could effectively engage with their patients. Interest in food, nutrition, and diet 
(79%) and in exercise and physical activity (72%) were widespread both among the study 
population and nationwide (173); patients were also interested in their own medications 
(66%) and their own (71%) and others' (71%) diseases and illnesses. Given the 
population-level association between general health status of good or better and health 
information use, pursuing HIT solutions in this area may offer particular value to both 
patients and providers. Similar indications are observed in findings from a national 
health IT consumer survey, in which 55% of adults expressed interest in HIT web sites or 
applications, including specific interest in tracking information about chronic illnesses 
(42%), tracking diet and nutrition (36%), tracking exercise and physical activity (33%), 
and for medication reminders (30%) (174). 

99 



Within-tier findings regarding associations between health status measures and 
health information use are of interest as well, given that risk stratification was based on 
CDPS risk scores and chronic disease status for diabetes and hypertension, and thus 
indicative both of present and projected future health outcomes. Tier 1, the lowest-risk 
and healthiest tier, showed no association between health status measures and health 
information or communication; neither did Tier 3, where health status measures were 
significantly skewed toward the unhealthier end of the spectrum. While the former might 
be due to lack of interest related to patients in the lower-risk tier having a generally-better 
overall health status or to there being insufficient power to detect smaller effects in a 
healthier subpopulation, the latter finding is somewhat surprising. One possibility is that 
patients in Tier 3 may have already reached a point where the increased power and 
increased agency associated with patients' knowledge of health information in a 
Foucaldian framework may be insufficient to effect significant change on health 
outcomes in the face of other factors. If true, this suggests that HIT-based interventions 
with this higher-risk population may be of more value when involving active engagement 
between patients and health providers in the context of using health information to 
improve coordination of care than approaches that depend on patients' own ability to 
translate information into knowledge and put it into practice unaided. In addition, the 
finding of a within-stratum association between good general health status and use of 
health information in Tier 2 - the intermediate-health tier - indicates the existence of a 
possible intervention point where HIT may be effectively used to either delay or 
ultimately avoid poor health outcomes. This potential opportunity for increased 
effectiveness should be taken into consideration when designing patient-centered HIT- 

100 



based health interventions. Additional benefit may be gained by conducting follow-up 
research to examine tier-specific results in greater depth through collecting comparable 
data from a larger sample of the population within each risk tier. Another approach would 
be to examine the longer-term effects of IT and HIT use in a cohort over time. 

Despite the observation of defined and significant patterns of IT and HIT 
utilization among the study population, the findings from this study do not disprove the 
continued existence of the digital divide; instead, they affirm it. The digital divide was 
observed to exist between the study and national populations, taking the overall shape of 
"timeshifted" IT adoption rather than technology nonuse. For example, 31% of people 
nationally use tablet computers (125), versus 23% in the study population; 26% 
nationally use e-readers, versus 17% in the study population; and 67% nationally are on 
Facebook (175), versus 57% in the study population. Adoption of IT devices and 
activities in the study population can be theorized to lag national adoption, increasing 
over time as price or other barriers to use decrease. For example, examination of the cell 
phone adoption timeline (Figure 10) shows a spike in cell phone use of less than a 
month's duration. It is possible that this increased recent adoption may be attributable in 
part to the comparatively new availability of free and reduced-price, subsidized Lifeline 
wireless service to qualifying recipients in the state of Colorado, lowering barriers to 
access and enabling patient-workers to obtain this particular technological means of 
production. 

The divide was clearly seen within the surveyed population as well. Use was 
observed to be significantly higher among younger people than among seniors, 
representing the existence of the "gray gap" in technology use (125, 166, 167). Primary 

101 



language was also observed to be a barrier, as English speakers were significantly more 
likely than Spanish speakers to use computers, the Internet, health information, and most 
other types of other common technology with information delivery capacity. 
Race/ethnicity and gender were also significant factors affecting use and nonuse, with 
Hispanic/Latino patients less likely to use computers and the Internet, whites less likely 
to use cell phones, health information, and health communication, and women less likely 
to use IT or HIT solutions than men in general. In addition, technology nonusers 
consistently reported barriers of knowledge, access, and cost that hindered their ability to 
utilize IT and HIT, while at the same time expressing interest in overcoming these 
barriers and crossing the divide - findings that reflect similar observations both in safety 
net settings (176) and nationally (94, 95, 173). 

These hindrances are far from insignificant in light of the finding that the 
Foucauldian power-knowledge dyad was observed to be applicable through the 
population-level and Tier 2 associations between accessing health information and good 
general health status. These findings indicate support for the premise that people can 
assume greater agency over their own health status and improve their health outcomes in 
the presence of health information, which consequently indicates the importance of 
assisting patients to obtain such agency in the first place. The health inequity represented 
by the impact of the digital divide on accessing health information and achieving the 
associated better health may well be measured in the cost of care, in morbidity, and in 
mortality. 

One possible approach to overcoming the deleterious effects of the digital divide 
is to utilize the concept of the Foucauldian economy of power to disseminate power- 

102 



knowledge across the divide through the influence of social networks. In order to assess 
the potential to conduct such dissemination activities within this population, the third aim 
of this study was to evaluate the applicability of traditional Dol theory when used to 
examine patterns of adoption and utilization of health IT among adult patients who 
receive care in an urban safety net setting. It was hypothesized that members of priority 
populations would have an interest in using IT to access health information and to engage 
in health communications that is equivalent to that reported among members of more 
advantaged populations, but would not use the same types of IT in the same manner or to 
the same extent. 

Health communication findings from this study indicate that patients consider 
their health-related social networks to primarily include their families (88%), their friends 
(75%), and their health care providers (71%). The identification of distinct adopter 
categories within the study population which show no significant difference in their 
population distribution from that of the Dol model population supports the use of Dol 
theory within this single broad population stratum as well as across population strata. 
This offers the opportunity to use diffusion research principles with this population, 
including the chance for DH health care providers to consciously act as leaders and 
"change agents" within patients' health-related social networks. The role of health care 
provider as change agent is additionally supported by studies indicating that patients are 
more likely to engage with and have confidence in IT systems that providers use or 
recommend (174, 177, 178). 

In addition to addressing the challenges of cultural hegemony through patient- 
centered approaches to health informatics solutions as discussed above and through 

103 



current initiatives to actively collect informed consent and patient-provided preferences 
for IT-based communication methods and topics, DH could act at the level of the Marxist 
capitalist system to alleviate the separation between patient-workers and the output 
commodity of their health information. Using the meaningful use criteria established 
under the HITECH Act and the recommended standards and solutions under the ACA in 
combination with the Chronic Care Model as a guide, the operational infrastructure could 
be altered through implementing changes to DH's electronic medical record, clinical 
information and decision support systems to lower specific barriers to access in ways that 
are particularly applicable to and supportive of patient needs. Themes emerging from the 
qualitative results for this study suggest areas of potential opportunity for such system 
redesign that are of interest to DH patients, such as making information available through 
patient portals, providing patients with the ability to schedule appointments online or by 
email or text, and establishing electronic channels of communication between patients 
and their care providers, such as by email and text message. DH is also uniquely 
positioned to represent the needs of priority populations in a leadership role in certain 
areas on a larger scale; for example, despite patients' expressed preferences and granting 
of informed, explicit permissions, the implementation of the HIPAA Privacy and Security 
Final Rule in January, 2013 limits what health information can be exchanged through 
widely accessible electronic methods such as text messages (179). By working in 
partnership with its patient population to develop patient-centered informatics solutions 
that are tailored and appropriate to population needs, DH may be able to provide insights 
that have the potential to effect needed change within the greater societal superstructure. 



104 



In a more immediate, interpersonal sense, DH providers could interact as change 
agents with interested patients to overcome knowledge barriers through such means as 
the provision of technology training classes, conducted similarly to group education 
sessions held on topics such as nutrition and weight management. Providers and care 
teams could be trained and engaged as well to overcome knowledge gaps and bias 
inherent in possible misperception of patients' technological capabilities, and could then 
work appropriately in partnership with patients at the point of care to identify low-cost, 
accessible channels for IT and HIT use as well as to address patient concerns about IT 
and HIT, such as concerns about the privacy and validity of health information obtained 
through IT and the perceived potential of IT solutions to be impersonal and distancing 
from their health care providers. 

Additional future work to inform this area might involve conducting detailed 
analyses to compare population-specific Bass model coefficients of innovation and 
imitation (113, 180) for known technologies to the coefficients observed to exist in the 
larger population. If equivalence between coefficients is observed, diffusion modeling 
could then be conducted using the larger-population coefficients to reliably predict 
technology adoption in vulnerable populations. This predictive modeling could be used to 
refine IT and HIT development strategies to improve targeting and delivery of patient- 
centered, tailored appropriate health informatics solutions which could be designed in a 
timeframe that would support use at the timeshifted point when such technologies hit the 
productivity plateau in the larger U.S. population and begin to move up the adoption 
curve in the study population. By actively creating patient-centered solutions for 
implementation across the timeshift, the DH health care system and its patient population 

105 



thus have the chance to work together in partnership to reduce the impact of IT-related 
health disparities and close the gap of the digital divide. 



106 



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121 



APPENDIX A 



CHI SURVEY INSTRUMENT (ENGLISH) 



Consumer Health Informatics (CHI) Survey 

As a Denver Health patient, you have been randomly selected to participate in a research study. This 

study is designed to team about how people can use information technology like computers and 
cellphones to get information and to talk with others aaouf meir health and health care. 

If you deride to join this study, please answer m ouesflQm in ms survey. You can complete this 
survey in one of two ways: 

1) You can answer the questions in the enclosed paper survey and return 
it to us in the included envelope We have already paid ihe postage for you. 

2) You can also fill out the survey online at: 

https : //www survBvmonkBv.com/s/DenvBrHeallhCHI . 
The webpage will ask you to type in a survey ID Your survey ID is . 





PI: Susan L. Moore. MSPH 
COM IRB #12-1099 



U-vrl One Care For ML 



Introduction 



You have a choice about being in this study You do not have to be in this study if you do not want to 
be. If you decide not to be in this study, it will not have any effect on your medical care. 

We do not think that there are any ris^sto you from being a part of this study. bLt there may be some 
risks we have not thought of. We will not include any of your personal health information in the survey, 
and we will do our best to protect your privacy. 

If you have any questions, you can call Ihe principal investgalor, Susan Moore, at 303-602-2744. You 
can call and ask questions at any time. If you have questions about your rights n Ihe study, you can 
also call COMIRB (the responsible Institutional Review Board) at 303-724-1055. By completing the 
survey, you are agreeing to participate in the research study. 



Thank you for your consideration! 
To begin the survey, please turn the page. 



EHDknykh 



Survey Sections 



This survey has sw sections Tor you to complete. We will be asking qjesfJons about your health in general, now you use 
computers and cell phones how you use (he intranet how you team about health information, how you talk with 

others about health, and your opinions about technology 



Parti: Your Health in General 



1 ) Would you say that in gerterat your health is- 

Excellent □ 

Very good □ 

Good. □ 

Far. □ 

Poor □ 



2 ) Now thinking about your physical hea'th which 
includes physical stfness and injury, for how many 
days dunng the past 30 days was your physical 
health HQT good? 

N umber of Days 



My physical health was good every da/ ! I 



Now thinking aboil! y->:.i mental r.eaHh, which 
tndudes stress, depression, and problems wtth 
amotions, For how many days dunng the past 30 
days was your marital health NOT good? 

Number of Days 



My mental health wa* good every day. □ 

4.1 Ounhg the pail 30 days, for about how many days 
did poor physical or nental health haop you from 
doing your usual actr/rttes, such as self-care, work, 
or recreation? 



Number of Days 



I could do my usual activities: every day. □ 




Fart l\ Using Corrwere and Cell Phones p.j 

In this section we will be asking questions about how you use computers and cell phones such as how often you use 
them, what kinds of theni you use. what yoj use them for and whal makes using them easy or hard for you This will 
help us learn aboui what you want and need from information technology like this There are no wrong answers 



^ 5ji Do you use a computer, at least sometimes? 

NO □ Yes □ 



> 



5; 



SHIP to page 3 



CONTiNJE boiow 



What kind of computer do you use? 
Please mark alt that apply. 

Desktop □ 

Laptop or notebook □ 

Tablet (like an tPad or Galaxy) □ 

Another kind; 



7 ) How long have you been u$rng computers * 

Less than 1 month □ 

From 1 to 6 months .... □ 

7 months to a year □ 

i or 2 yean □ 

3 to 5 years □ 

5 to 10 years □ 

More than IQyeara □ 

3 Dower 



< 



< 



About how often do you use a computer? 

Several times a day. □ 

About once a day □ 

3 to 5 days a week. ,. □ 

1 or 2 days a week „ . □ 

Every few weeks D 

Once a month or less often □ 



How important >s >' to you to be able to 
use a computer 7 

Very ot always important □ 

Usually or mostly important □ 

Sometimes important □ 

Rarely or only a littJ? important □ 

Not important D 



< 




123 



Part 2: Using Computers and Cell Phonos 



11 ) if you don't use a computer why not* 
Pteitss mark aii that appty. 

I don't have one-, and there a not one- 

anywhere I can use D 

I don't know how to □ 

I don't need to □ 

I don't want to, or I'm not interested □ 

It's too hard, or I get frustrated by rt_ □ 

It's too expensive □ 

it's a waste of brine □ 

Some other reason (please tell uswiy). □ 



V 



12.) ff yon don't usa a computer. MjuftfywAfos-ro* 
Please mark fit that apply 

Ym □ 

No □ 

Maybe, rf I had one □ 

Maybe, rf it didn't cost loo much O 

Maybe, rf I knew how □ 

I don't know/ I'm not sure □ 



dllKMTH 



13} If you don 't use a computer what would make 
It easier for you to use a computer? 
Please marfc all that appty. 

Lessons on how to use computers C 

Lower cost, so that) could afford one □ 

Having computer programs and 

information m my native language... . D 

I don't know. □ 

Something else i pi ease tell us what) LJ 




Fart l\ Using Compmere and Cell Phong? 



^ 14 ) Do you use a cetf phone at least somt-nmes 

NO □ Yes □ 



> 



SWP ro page 5 



CONTINUE below 



15.) 



What kind ot caf phone do you use? 
Ptoase mark, off that apply. 
A "smart" phone (like an iPhone 

or Android) □ 

A regular or basic phone □ 

Another Kindt 



< 



16 ;< How long have you been using emit phonos' 

Less lhan 1 month □ 

From 1 to 6 months □ 

7 months to a year □ 

1 or 2 yean □ 

3 to 5 yean □ 

5lo IQyears □ 

More than lOyears 



< 



> 



About how often era you use a coil phono? 

Several times a day. □ 

About once a day □ 

3 to 5 days a week □ 

1 or 2 days a week □ 

Every few weeks □ 

Once a month or less often □ 



1 3 1 hfow important >z >' to you to be able to 
use a c*M phorw 7 

Very or always important □ 

Usualty or mostly important □ 

Sometimes important □ 

Rarely or only a little important □ 

Not important □ 



< 




lliKwra 

jtlm:rn 




124 



Part 2: Using Computers and Cell Phonos 



if ycni don't ust> a ceif phone why not? 
Ptesse merit ait that appfy. 

I don't have one-, and there's not one 

anywhere I can use Q 

I don't know how to □ 

I don't need to □ 

I don't want to, or I'm not interested □ 

irs too hard, or I got frustrated ty it.. □ 

Its too expensive □ 

ir* a waste erf brne □ 

Some other reason (please tell us w»y).. □ 



21.) ft you don't use a ceif phone would you 
Weo to 7 Ptesso mark all that appry 

Y«* □ 

No □ 

Maybe, ~t I had one □ 

Maybe, rf it didn't cost loo much □ 

Maybe, rf I knew how □ 

I don't Know/ I'm not sure 



11JKNYKR 
d I IK MTU 



P-8 



if you don't use a cellphone what would 
make it easier for you to use a ceft phone 7 
Please mark aiithat apply. 

Lessons on hc-Vi'louse cell phones C 

Lower cost, so that I could aifford one □ 

Having cell phone programs and 

information m my native language □ 

I don't know. □ 

Something else [please tell us wtiat) LJ 




Fart l\ Using Computers and Cell Phong? 



23 ) Do you u$$ e computet and/or a cati phone to do any of the tyfowing things, at least sometimes? Ptea.se mark mtf 
tha! appfy if you do any vf these things with h$fh computer* an& cei f phones, please mark both botes 







Cell Phone 




8tnderr*e«h*«ml 




□ 




s* **a or f*c*<ve ton m»»go» 

Make or rtHttrvO voKo calli 


□ 


□ 


□ 


Watch ' acq: > ic on VouTubc) 








Mako and pe>H yorjf o«vn viSto* (I** on Y<MjTufc*) 


□ 







Ptay or toon to rnuuc 








Read poii at comment «n Faeeboek 


□ 


□ 


□ 


Read or port to TwrBer 




□ 




Way mo 5 jrturrs*/ 


□ 


□ 


□ 


Flay fljt rrtfri MA orfw P*Q&* 








Wnto jrrf poll to >W Oivn btofl or gninp journal 


□ 


□ 


□ 


fttad Of wmmant 0f> attw pwpfa's M*as or onlM jcurnai fxnb 








Haad, post, or tiia ro rJrurtflton wabtrtas l*o Piwerc^i 


□ 


□ 




LOO* el or pott p*«ur« on towegrem or Fiic*« 








Talk M people wth vrieo dut or Skype 


□ 


□ 


□ 


Talk to pcepto one on on*, Mce wHi AIM , YihoolMeswnaer , or Googk Chan 








Talk to group* of poopl* n chjl nwrns 


□ 


D 


□ 


Road -•>: ■ 








Wateh TV show* or mow* 




□ 


□ 


Look up mfoi itiaman 









Ctfiw (hind* w*h compuen'r Ploa*0 taB u* what 

Qhtr thirtQ* cell phonet? Please (ell u& whal 



RHIJKNYEB 
OlFJIXll 




Part 2: Using Computers and Cell Phones 

/T 24. } Da you use a computer cett phom. or other N. 
device- to keep track of informal/on about vOur 
tBS/th of least somelimes 7 Health information 
can include things like what medicines you take, 
when your medical appointments are, looking up 
things hke tab test results or information about 
a disease and home monitoring of things like 
your blood pressure, blood sugar, how you are 
feeling, and how much you exercisa 

Please mark aff that apply 

Computer □ 

Cell phone □ 

Another device □ 

I don't do this □ > 



*7 



2i ) Do you use a computer, cell phono or other \ 
dovice to talk to other people about health Or \ 
t)SSAh care, at feast sometimes? This is called 
health communication, and can include things 
like calling a provider to ask questions between 
visits, catting emailing or texting people you 
know to talk about health or health care, and 
posting comments, questions or stories ebovt 
health online for other people to see and reply to. 

Please mark all that apply 

Computer □ 

CeN phone □ 

Another device □ 

I don't do this □ / 



26.} Are there things you would like to use computers to do, but can't? Please tell us what and why. 



27 } Aip there things you would like to <jse cellphones to do hut can't' 9 Please iefi us what and why 



d I IK MTU 




Part 3; Using the Internet * 

In this section, we wll be asking questions about If and how you use the Internet or go online such as how often you 
go online how long you have been gang online, and what makes using the Internet easy or hard for you This will 
help us learn about what you want and need from online resource* such as websites There are no wrong answers 



28 ) Do you use the internet ( go online % 
at least, sometimes? 
Wo . D Yes □ 



> 



SKIP to page 9 



CQNTiNJE below 



How do you access the Internet /(go online'} 7 
Please mark aii that apply. 

Desktop computer □ 

Laptop, notebook, or owet computer □ 

Cell phone □ 

Another way: 

30) Howtong have you been m<ng the Internet'* 

Less than 1 month □ 

From 1 to 6 months □ 

7 months to a year □ 

i or 2 yean □ 

3 to 5 years □ 

5 to 10 years □ 

More than 10 years □ 

3 Dower 
dilmxH 



< 



31 > About how often do you use the Internet 7 

Several times a day. □ 

About once a day □ 

3 to 5 days a week □ 

1 or 2 days a week □ 

Every tew weeks □ 

Once a month or less often □ 



^32) 



How important >s >' to you to be able to 
use the Internet (go online) 7 

Very or always important □ 

Usually or mostly important □ 

Sometimes important □ 

Rarely or only a littl? important □ 

Not important □ 

33. ) Do you have broaoband or high-speed 

Internet access? 



< 




126 



Part 3: Using the Internet 



P.9 



34 > If you don't use the Internet (go online), 
why nor? Please mark all that apply 
I don't have access to it at home, 
and there's not a nowhere else 

I can use it D 

I don't know how to □ 

l don't need to D 

I don't want to, or I'm not interested □ 

lis H<x> hard, or I get frusirated by it □ 

It's loo expensive □ 

Its a waste ol time U 
Some other reason (please tell us wry).. □ 



if you don't use the internet (go online), wiiat > 
would make it easier for you to use the internet? 
Please marff alfthat apply. 

Lessons o n h ow to use the I nternet. . . . C 
Lower cost, so thai l could afford 

access to the Internet JD 

Having information available 

■n my native language Q 

I don't know. □ 

Something else (please tell us what) □ 



35.) if you don't use the internet (go online}, *\ 
would you rVfce to' Please mark ail (hat apply 

y« n 

No □ 

Maybe, N I had access □ 

Mayoe, if it didn't cost too much □ 

Maybe, rf I knew how □ 

I don't know/I'm not sure □ 



11JKNYKR 
d I IK MTU 




Part 4: Lamina Atout Health information Through Technology P io 

In this section, we will be asking how you learn about health information Heallh information can be about things like 
medicines yc j take, when your medical appointments are lab test results, or information about a disease or how you 
are feeling. This will help us team what kinds of health information you want and need Tne<e are no wrong answers 



37 l Do you use a computer eeii phon* or other 
device ta took up health information, el least 
sometimes? 

No. □ Yes O 



SKiP fo page 12 



CONTiNJE betovs 



38 ) Do you look up health information for yourself 

or for somoorw el so ' Pfe&sa mark alt thai appty. 

Fof myself □ 

For someone else .... □ 

^39 ) How long have you bean fooktPQ up 
twaiih information'' 

Less than 1 month □ 

From 1 to 6 month* „ □ 

7 months to a year □ 

1 or 2 years □ 

3 to 5 yeare □ 

5 to 10 years □ 

^ More than 1 years □ y 

j hi: mi h 



A 3 ) About how often do you look up health 
information? 

Several bmes a day □ 

About once a day □ 

3 to 5 days a weak. 

1 or 2 days a week □ 

Every Tew weeks □ 

Once a month or less often □ 



f 4V> 



How important js >' to you to be able to 
look up hoafth information 7 

Very or afways important □ 

Usualfy or mostly important □ 

Someti mes i important □ 

Rarely or only a Irttls important. 

Not important □ 




127 



Part 4: Learning About Health Information Through Technolog y P n 



42 '/ What kmd$ of health information cfo you look up. at teas? sofnofwnes' Pfeese mark ajf rftaf aopry 

Y«» Mo 



About a disejiso or Ann you have 


□ 




About a di»a» or wimwnt you know ha* 






Afcoul »uffl*fy you 3io goog la have 


□ 


□ 


About sutgwry aOmKinfl yOU know ft 5»Wig tO hd^O 


About how you are foiling 


□ 


□ 


About modcirws you take 






Afcoul madtcimi lannvno you know tik*s 


□ 


□ 


.".T--1.1 Tnlln inmnnH 


About doctors or oihcr hoaltheaio providers 


□ 


□ 


Lab tost results lor yourcarf 


Lab tost results tor somaon* you know 


□ 


□ 


Notoi from your toil appomtmant w#i my hoakh car* provider 


Not« Irom wmoorta oiv s appgrHmcrf vwith thffir hwfth provider 


□ 


□ 


About oxardao or physical activity 


About food, niftbon, or dnl 


□ 


□ 


AjkuI birin conVoi or torrefy planning 


About ha btta you want to ehango tkka drinking or snicking) 


□ 


□ 



^iKiJl h^nMh Igpcs in Ihg ,\: 



JDfMVER 
d I IK MTU 



* Uf.lv. CCW'RBtlMOW 




Fart 4: Learning About Health information Through Technology 



43 ) tt you don 't use a computet cell phono 

or other device to look up health information 
why not? PSaase mark alt thai appty 

I don't know how to □ 

I don't need to □ 

I don't want to, or 3 m not interested O 

If 6 too hard, or I get frustrated by it □ 

I don't understand the health 
information I find D 

I don't trust the health 
information I find □ 

I would rather ask my health care 
provider in person □ 

H am worried about my pnvacy □ 

Some othei reason (pi ease tel I us wiy). . □ 



j Hi: Ml H 



44 i i'r" yOiJ don 't use a computer, caff phono 

or other device to ook up health information, 
would you like to? Pteasa mark all that apply 

Y*» □ 

No □ 

Ntaybe, if I had a way to □ 

Maybe , if it didn't cost too much □ 

Maybe, if i knew how □ 

hrlaybe, if I was sur« my personal 
information was safe and private Q 

I don't know / I'm not sura D 




H5P 



128 



M3 



Part 5: Talking With Others About Health Through Technology 

In this section, we will be asking questions about how you talk to other people about health o' health care. This is called 
health common leal Ion. and can include I lungs like calling a provider to ask questions, emailing or testing people you 
know about health or health care r and posting comments, questions or stories about health online for ether people to 
see Thta vwll help us learn what kinds oi health communication you want and need The*e are no wrong a 



45 ;■ Do you use a computer ceil phont. or other 
device to talk to other people about health or 

health care, at feast sometimes? 

No, . ,.□ 
SKIP lo page 15 



CONWN\JE below 



How long have you been talking to others 
about hea!th or health care this way 7 

Less lhan 1 month., □ 

From 1 to 6 months „ □ 

7 monihs to a year □ 

1 or 2 years □ 

3 to 5 years □ 

5 to 19 years. □ 

More than 10 years □ 



< 



JDfMVEK 
d I IK MTU 



47 i About how oft on do you talk to others about 
health or haaltrt cam this way? 

Several times a day □ 

About once a day □ 

3 to 5 days a week. □ 

1 or 2 days a week □ 

Every few weeks □ 

Once a month or less often □ 



How important >s r to you to talk to others 
about health or health care this way? 

Very or always important □ 

Usually « mostly important □ 

Sometimes important □ 

Rarely or only a lift? important, □ 

Not important □ 



< 










Part 5; Talking wtth Others About Health Through Technology 








49 1 Who tioyoutalkto eboiA heattf} or health care through computers ceil phone 
pfaase mart att that apply 


S. Of Olrtttr tSttvtCQS 7 
Yu 


Ho 


v*nf c are pf*vt*f 


□ 


□ 


'<'-:-.. i f.l 111 , 






YourMtne* 


□ 


□ 


Poopkf you met onlrw 






Roople you work wtri 


□ 





Pto&pfa W 0* fa MhAAl wth 






FtopJfl- in you" rwighbgihcKKlQr coimwly 


□ 


□ 


YHvf prieit. pfQ*chflr. gr Q&wjr rcligiguj kaOsr 






Someone *l».'cith»r people yw know i p*eaw te« u* **k>) 
















50. ) Are them pftoplo you would tike to tattt to this way. but can't? Pfaase toil us h 


ho they an ami wh} 


you can't 
















J 






DENVER 
ailKAI.Tli 






1 


m 











Parts: Talking With Others About Heallh Through Technology 



51 J if you don't use a computer ceti phone 
or other device to talk wrth others about 
health or heafth cars, why not? 
Pieasa mark alt thai apply. 

I don't know how lo □ 

I don't need to □ 

I don't knew anyone to talk to, □ 

I don't want to, or I 'm not interested □ 

its too hard, or i get frustrated by it. □ 

I don't understand what they 
teCl me that way □ 

I would rather ask rny neallh care 
provider in person D 

I am worried about my pnwacy □ 

Some other reason (pf ease tell us wiy).. □ 



ilJKNYKR 
dllKMTH 



SI ) it you don 't use a computer, ceff phone 

or other device to \afk with others about health 
or heafth care, would you like to? 
Please mark alt that apply 

Yes □ 

No □ 

Maybe, if i had a way to □ 

Maybe, if it didn't cost loo much O 

Maybe, if I knew hew, □ 

Maybe, If I was sure my personal 
information was safe and pnvate □ 

I don't know / I'm not sure □ 




Pa rt 6: Your Ooi n ions Abo ut Tech no loo v 


p- *• 


S3. ) hfave you aver used any of these otter things, at teasf sometimes? Pta&sa mark all that appty 




Ym 


Ho 


A tprcai d*ww just tor hnpng track of yaw health, like ■ btood sugar toiler (flluanreter). 
* blood pr«uuro cuff, or a stole to yk irjh yourootf on' 





An e-bwk reader, like * Kindle or Nook? 


An MP3 or rrwlc pityar. tke in iPod? 


□ 


Aflame ctnsokt. aha a Xfc.ii. PlaystirliWV 0* Na^antt^ 




A DVD playvr «t Bhi-Rty pttfT? 


□ 


Cab*c TV thil hra you watch iwrai or TV show* you pick "on demand"? 


A epocul box ttul tola you watch movies or TV show, onlrte, Ifca a RoVu, 8o>;ee e 1 Apple TV? 


□ 


Special faalurai on j TV that let you watch mo-.n>i or TV shorn ertfr/M? 



) Do you hsve any thoughts opinions of comments ebout inlonnaiion technology (Me computers and 



ceftptnnes) either in general or wtten used fur health or health cere lhal you would like to share 7 



iito.\m\ 



130 



APPENDIX B 



INVITATION LETTER AND MAILING LABELS (ENGLISH) 



I Denver 
IHealth 

Irtv! One t'.arc for Al l, 

FebruaryU ,2013 

[FIRST NAME CASE) [LAST NAME CASE) 

[ADDRESS 1 CASE] 

[ADDRESS_ADDL CASE] 

[ADDRESS 2 CASE] | ADDRESS 3] [POSTAL_CD] 



Dear FIRST NAME CASE: 



You have been randomly selected as part of a group of Denver Health patients who are being 
asked to be part of a research study. This study is designed to learn more about how 
technology like computers and cell phones can be used by patients to get health information 
and to talk with others about their health and health care. 



In the next two weeks, you will recede a survey in the mail. If you decide to join this study, we 
will ask you to answer the questions in the survey. You can fill the survey out online or on 
paper. We will pay for postage. It will not cost you anything to complete the survey. 

You have a choice about being in this study You do not have to be in this study if you do not 
want to be. If you decide not to be in Ihis study, it will not have any effect on your medical care. 
We do not think that there are any risks to you from being a part of this study, but there may be 
some risks that we have not thought of. We will not include any of your personal health 
information in the survey, and we will do our best to protect your privacy. 

If you have any questions, you can call the principal investigator, Susan Moore, at 303-602- 
2744. You can call and ask questions at any lime If you have questions about your rights in 
the study, you can also call COMIRB (the responsible Institutional Review Board) at 303-724- 
1055. 



By completing the survey, you are agreeing to participate in the research study. Thank you for 
your consideration! 

Sincerely, 



Susan L Moore, MSPH 
Principal Investigator 
COMIRB #1 2-1099 



77?BiHiBdiStset Mai Code 3210 Dem-a-, Colo&Jo S02D4 Wet* 303-602-2744 Fix 303-602-2741 



?a Denver 
Health 

Level One Care For ALL 
777 Banrucfc Si. 
Denver CO 802O4-45O7 


1001 


Alln: S.L Moore 
MC 3240 


JANE DOE 
123 MAIN ST 
DENVER, CO 80202 


#i Denver 
P Health 

Level One Cars Fat ALL 
777 Bannock Si.. 
Denver. CO 80204^4507 


Attn S.L. Moore 
MC 3240 


JOHN SMITH 
123 ANYTOWN AVE 
APT 1 

DENVER, CO 80202 


1% Denver 
^Health 

Level One Car? Ft* ALL 
777 Bannock Si., 
Denver CO B02O4-4507 


Attn: S.L Moo-e 
MC 3240 


?a Denver 
Health 

Level One Cars For ALL 
777 Ranm.-. S! , 
Denver. CO A02O445O7 


TOO 7 


Aim S L Moo-e 
MC 3240 


?A DENVER 

health 

Level One Cam For ALL 
777 Bannock S: . 
Danvaf. CO 80204-4507 




Attn: S.L. Moore 
MC 3240 



^DENVER 
MLEAETI1 

Level One Care For ALL 
777 Bannock St . 
Denver CO 60204-4507 



Attn: S.L. Moore 
MC 3240 



DENVER 

eshealth 



Attn: S.L. Moore 
MC 3240 



^DENVER 

iMIhealth 



Attn: S.L. Moore 
MC 3240 



I\1DENVER 
IMILEACITJ 



Attn: S L. Moore 
MC 3240 



£\]DENVER 
^HEALTH 

Level One Care For ALL 
777 Bannock 31 
Danvw. CO 60204-4507 



Attn: S.L. Moore 
MC 3240 



132 



APPENDIX C 



REMINDER POSTCARD (ENGLISH) 



I .ast w-eek a survey was mailed to you to ask your opinions about how people can use 
infonnation technology 1 , like compute is and cell phones, to gel information and to talk 
with others about Ihcir health and health care. You were one of several Denver Health 
patients who were randomly selected to receive tliis survey as part of a research study. 




If you have already completed the survey and returned it to us eilher online or in the 
mail, we thank yon very much for your help. If you have not yet liad a chance to 
complete the survey, we ask vou to fifense consider doinz so totitiw If von have 
misplaced y our copy of the survey, we will send you another one shortly. 




Your kelp is vary important to tts. because it w ill help as learn how to heller use 
technology' 1 to provide vou with health information and talk with vou about your health 
and health care in ways that you prefer. 

You have a choice about whether or not to complete this survey. You do not have to 
fill out the survey if you do not want to, and if you decide not to it will not have any 
effect on your medical care. By completing the survey, you are agreeing to participate 
in our research study . Thank you for your consideration! 


JANE DOE 
123 MAIN ST 
DENVER, CO 80202 


Sincerely, 




Susan L. Moore, MSPH 
Principal Investigztor 

t'OMIKH Protocol J? 12-1 099, approved September 28, 2012 





Last week a survey was mailed to you to ask your opinions about how people can use 
infonnation technology , like computet^ and cell phones, to get infonnation and to talk 
with others about their health and health care. You were one of several Denver Health 
patients who were randomly selected to receive tliis survey as part of a research study. 




If you have already completed the survey and returned it to us eilher online or in the 
mail, we thank yon very much for your help. If you have not yet liad a chance to 
complete the survev. we ask vou to chase consider doinz so todav. If vou have 
misplaced your copy of the survey, wc will send you another one shortly. 




Your help is very important to us, because it will help as learn how to heller use 
technology to provide you with health information and talk with you about your health 
and health care in ways that you prefer. 

You have a choice about whether or not to complete this survey. You do not have to 
fill out the survey if you do not want to, and if you decide not to it will not have any 
effect on your medical care. By completing the survey, you are agreeing to participate 
in our research study. Thank you for your consideration! 


JOHN SMITH 

123 ANYTOWN AVE 
DENVER. CO 80202 


Sincerely, 




Susan I.. Moore. MSPH 
Principal Investigator 

COMIRH Protocol tf 12-1 099, approved September 2K, 201 2 





133 



APPENDIX D 



CHI CODEBOOK 



Part 1: Your Health in General 



Variable Name: 


CHI-Q1 


Variable Label: 


CDC HRQOL - 4 , item 1 


Question Text: 


Would you say that in general your health is: 


Values & Labels: 


1 - Excellent 

2 — Very good 

3 - Good 

4 - Fair 

5 - Poor 

7 - Don't know/not sure 
9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q2 


Notes: 


Questions and coding: 

http://www.cdc . gov/hrq ol/hrq ol 1 4 measure. htm# 1 


Methods, measures, and SAS syntax: 
http://www.cdc.gov/hrqol/methods.htm 




Variable Name: 


CHI-Q2 


Variable Label: 


CDC HRQOL - 4 , item 2 


Question Text: 


Now thinking about your physical health, which includes physical 
illness and injury, for how many days during the past 30 days was 
your physical health not good? 


Values & Labels: 


- number of days 
88 - None 

77 - Don't know/not sure 
99 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q3 


Notes: 


Questions and coding: 

http://www.cdc. gov/hrqol/hrqol 1 4 measure. htm# 1 


Methods, measures, and SAS syntax: 
http://www.cdc.gov/hrqol/methods.htm 



134 



Variable Name: 


CHI-Q3 


Variable Label: 


CDC HRQOL - 4 , item 3 


Question Text: 


Now thinking about your mental health, which includes stress, 
depression, and problems with emotions, for how many days during 
the past 30 days was your mental health not good? 


Values & Labels: 


- number of days 
88 - None 

77 - Don't know/not sure 
99 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


SKIP to CHI-Q5 if CHI-Q2 AND CHI-Q3 both equal NONE (88); 
Else CHI-Q4 


Notes: 


Questions and coding: 

http://www.cdc . gov/hrq ol/hrq ol 1 4 measure. htm# 1 


Methods, measures, and SAS syntax: 
http://www.cdc.gov/hrqol/methods.htm 




Variable Name: 


CHI-Q4 


Variable Label: 


CDC HRQOL - 4 , item 4 


Question Text: 


During the past 30 days, for about how many days did poor 
physical or mental health keep you from doing your usual activities, 
such as self-care, work, or recreation? 


Values & Labels: 


- number of days 
88 - None 

77 - Don't know/not sure 
99 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q5 


Notes: 


Questions and coding: 

http://www.cdc . gov/hrq ol/hrq ol 1 4 measure. htm# 1 


Methods, measures, and SAS syntax: 
http://www.cdc.gov/hrqol/methods.htm 



135 



Part 2; Using Computers and Cell Phones 



Variable Name: 


CHI-Q5 


Variable Label: 


Computer use assessment 


Question Text: 


Do you use a computer, at least sometimes? 


Values & Labels: 


1 - Yes 
2-No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


SKIP to CHI-Q11 if 2 (No); 
Else CHI-Q6 


Notes: 


Modified from Pew Internet & American Life Project - Digital 
Divisions Survey Topline (2005) 




Variable Name: 


CHI-Q6 


Variable Label: 


Computer type 


Question Text: 


What kind of computer do you use? Please mark all that apply. 


Values & Labels: 


See CHI-Q6 subset items 


Skip Pattern/ 
Default Next: 


CHI-Q6a 


Notes: 






Variable Name: 


CHI-Q6a 


Variable Label: 


Computer type = desktop 


Question Text: 


Desktop 


Values & Labels: 


1- Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q6b 


Notes: 






Variable Name: 


CHI-Q6b 


Variable Label: 


Computer type = portable, non-tablet 


Question Text: 


Laptop or notebook 


Values & Labels: 


1- Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q6c 


Notes: 





136 



Variable Name: 


CHI-Q6c 


Variable Label: 


Computer type = tablet 


Question Text: 


Tablet (like an iPad or Galaxy) 


Values & Labels: 


1- Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q6d 


Notes: 






Variable Name: 


CHI-Q6d 


Variable Label: 


Computer type = other 


Question Text: 


Another kind 


Values & Labels: 


1 - Yes (free-text value provided) 
2 -No (blank) 


Skip Pattern/ 
Default Next: 


If CHI-Q6d = yes, then CHI-Q6e; 
Else CHI-Q7 


Notes: 






Variable Name: 


CHI-Q6e 


Variable Label: 


Other computer type - detail 


Question Text: 




Values & Labels: 


Free text narrative content to be coded based on content analysis of 
responses 


Skip Pattern/ 
Default Next: 


CHI-Q7 


Notes: 






Variable Name: 


CHI-Q7 


Variable Label: 


Computer use duration 


Question Text: 


How long have you been using computers? 


Values & Labels: 


1 - Less than 1 month 

2 - From 1 to 6 months 
3-7 months to a year 

4 - 1 or 2 years 

5 - 3 to 5 years 

6 - 5 to 10 years 

7 - more than 10 years 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q8 


Notes: 


Modified from Pew Internet & American Life Project - Chronic 
Disease Fall Tracking Survey Topline (2008) 



137 



Variable Name: 


CHI-Q8 


Variable Label: 


Computer use frequency 


Question Text: 


About how often do you use a computer? 


Values & Labels: 


1 - Several times a day 

2 - About once a day 

3 - 3 to 5 days a week 

4 - 1 or 2 days a week 

5 - Every few weeks 

6 - Once a month or less often 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q9 


Notes: 


Modified from Pew Internet & American Life Project - Chronic 
Disease Fall Tracking Survey Topline (2008) 




Variable Name: 


CHI-Q9 


Variable Label: 


Computer use value 


Question Text: 


How important is it to you to be able to use a computer? 


Values & Labels: 


1 - Very or always important 

2 - Usually or mostly important 

3 - Sometimes important 

4 - Rarely or only a little important 

5 - Not important 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q10 


Notes: 


Modified from Pew Internet & American Life Project - Chronic 
Disease Fall Tracking Survey Topline (2008) 




Variable Name: 


CHI-Q10 


Variable Label: 


Computer ownership 


Question Text: 


Do you have a computer of your own? 


Values & Labels: 


1 - Yes 
2-No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q14 


Notes: 





138 



Variable Name: 


CHI-Q11 


Variable Label: 


Computer use barriers 


Question Text: 


If you don't use a computer, why not? Please mark all that apply. 


Values & Labels: 


See CHI-Q1 1 subset items 


Skip Pattern/ 
Default Next: 


CHI-Qlla 


Notes: 






Variable Name: 


CHI-Qlla 


Variable Label: 


Barrier = access 


Question Text: 


I don't have one, and there's not one anywhere I can use 


Values & Labels: 


1- Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Qllb 


Notes: 






Variable Name: 


CHI-Qllb 


Variable Label: 


Barrier = knowledge 


Question Text: 


I don't know how to 


Values & Labels: 


1- Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Qllc 


Notes: 






Variable Name: 


CHI-Qllc 


Variable Label: 


Barrier = need 


Question Text: 


I don't need to 


Values & Labels: 


1-Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Qlld 


Notes: 





139 



Variable Name: 


CHI-Qlld 


Variable Label: 


Barrier = interest 


Question Text: 


I don't want to, or I'm not interested 


Values & Labels: 


1 - Yes 

2 -No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Qlle 


Notes: 






Variable Name: 


CHI-Qlle 


Variable Label: 


Barrier = difficulty 


Question Text: 


It's too hard, or I get frustrated by it 


Values & Labels: 


1- Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Qllf 


Notes: 






Variable Name: 


CHI-Qllf 


Variable Label: 


Barrier = cost 


Question Text: 


It's too expensive 


Values & Labels: 


1- Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Qllg 


Notes: 






Variable Name: 


CHI-Qllg 


Variable Label: 


Barrier = value 


Question Text: 


It's a waste of time 


Values & Labels: 


1-Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Qllh 


Notes: 





140 



Variable Name: 


CHI-Qllh 


Variable Label: 


Barrier = other 


Question Text: 


Some other reason (please tell us why) 


Values & Labels: 


1 - Yes (free text response provided) 
2 -No (blank) 


Skip Pattern/ 
Default Next: 


If CHI-Q 1 1 h = yes, then CHI-Q 1 1 i; 
Else CHI-Q 12 


Notes: 






Variable Name: 


CHI-Qlli 


Variable Label: 


Other barrier - detail 


Question Text: 




Values & Labels: 


Free text narrative content to be coded based on content analysis of 
responses 


Skip Pattern/ 
Default Next: 


CHI-Q 12 


Notes: 






Variable Name: 


CHI-Q 12 


Variable Label: 


Computer use interest (nonusers) 


Question Text: 


If you don't use a computer, would you like to? Please mark all that 
apply. 


Values & Labels: 


See CHI-Q 12 subset items 


Skip Pattern/ 
Default Next: 


CHI-Q 12a 


Notes: 






Variable Name: 


CHI-Q 12a 


Variable Label: 


Interest = definite 


Question Text: 


Yes 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q 12b 


Notes: 





141 



Variable Name: 


CHI-Q12b 


Variable Label: 


Interest = none 


Question Text: 


No 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q12c 


Notes: 






Variable Name: 


CHI-Q12c 


Variable Label: 


Interest = moderated by access 


Question Text: 


Maybe, if I had one 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q12d 


Notes: 






Variable Name: 


CHI-Q12d 


Variable Label: 


Interest = moderated by cost 


Question Text: 


Maybe, if it didn't cost too much 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q12e 


Notes: 






Variable Name: 


CHI-Q12e 


Variable Label: 


Interest = moderated by knowledge 


Question Text: 


Maybe, if I knew how 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q12f 


Notes: 





142 



Variable Name: 


CHI-Q12f 


Variable Label: 


Interest = uncertain 


Question Text: 


I don't know / I'm not sure 


Values & Labels: 


1 - Yes (true) 

2 - No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q13 


Notes: 






Variable Name: 


CHI-Q13 


Variable Label: 


Computer use barrier mediation (nonusers) 


Question Text: 


If you don't use a computer, what would make it easier for you to 
use a computer? Please mark all that apply. 


Values & Labels: 


See CHI-Q13 subset items 


Skip Pattern/ 
Default Next: 


CHI-Q13a 


Notes: 


http://psvch.wisc.edu/henriques/mediator.html 


"The classic reference on this topic is Baron, R. M., & Kenny, D. 
A. (1986). The moderator-mediator variable distinction in social 
psychological research: Conceptual, strategic, and statistical 
considerations. Journal of Personality and Social Psychology, 51, 
1173-1182." 




Variable Name: 


CHI-Q13a 


Variable Label: 


Mediator = education 


Question Text: 


Lessons on how to use computers 


Values & Labels: 


1 - Yes (true) 

2 - No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q13b 


Notes: 






Variable Name: 


CHI-Q13b 


Variable Label: 


Mediator = pricing 


Question Text: 


Lower cost, so that I could afford one 


Values & Labels: 


1 - Yes (true) 

2 - No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q13c 


Notes: 





143 



Variable Name: 


CHI-Q13c 


Variable Label: 


Mediator = fluency 


Question Text: 


Having computer programs and information in my native language 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q13d 


Notes: 






Variable Name: 


CHI-Q13d 


Variable Label: 


Mediator = unknown 


Question Text: 


I don't know 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q13e 


Notes: 






Variable Name: 


CHI-Q13e 


Variable Label: 


Mediator = other 


Question Text: 


Something else (please tell us what) 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


If CHI-Q13e = yes, then CHI-Q13f; 
Else CHI-Q14 


Notes: 






Variable Name: 


CHI-Q13f 


Variable Label: 


Other mediator - detail 


Question Text: 




Values & Labels: 


Free text narrative content to be coded based on content analysis of 
responses 


Skip Pattern/ 
Default Next: 


CHI-Q14 


Notes: 





144 



Variable Name: 


CHI-Q14 


Variable Label: 


Cell phone use assessment 


Question Text: 


Do you use a cell phone, at least sometimes? 


Values & Labels: 


1 - Yes 
2-No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


SKIP to CHI-Q20 if 2 (No); 
Else CHI-Q15 


Notes: 


Modified from Pew Internet & American Life Project - Digital 
Divisions Survey Topline (2005) 




Variable Name: 


CHI-Q15 


Variable Label: 


Cell phone type 


Question Text: 


What kind of cell phone do you use? Please mark all that apply. 


Values & Labels: 


See CHI-Q15 subset items 


Skip Pattern/ 
Default Next: 


CHI-Q15a 


Notes: 






Variable Name: 


CHI-Q15a 


Variable Label: 


Cell phone type = smart 


Question Text: 


A "smart" phone (like an iPhone or Android) 


Values & Labels: 


1-Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q15b 


Notes: 






Variable Name: 


CHI-Q15b 


Variable Label: 


Cell phone type = regular 


Question Text: 


A regular or basic phone 


Values & Labels: 


1- Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q15c 


Notes: 





145 



Variable Name: 


CHI-Q15c 


Variable Label: 


Cell phone type = other 


Question Text: 


Another kind 


Values & Labels: 


1 - Yes (free-text value provided) 
2 -No (blank) 


Skip Pattern/ 
Default Next: 


If CHI-Q15c = yes, then CHI-Q15d; 
Else CHI-Q16 


Notes: 






Variable Name: 


CHI-Q15d 


Variable Label: 


Other cell phone type - detail 


Question Text: 




Values & Labels: 


Free text narrative content to be coded based on content analysis of 
responses 


Skip Pattern/ 
Default Next: 


CHI-Q16 


Notes: 






Variable Name: 


CHI-Q16 


Variable Label: 


Cell phone use duration 


Question Text: 


How long have you been using cell phones? 


Values & Labels: 


1 - Less than 1 month 

2 - From 1 to 6 months 
3-7 months to a year 

4 - 1 or 2 years 

5 - 3 to 5 years 

6 - 5 to 10 years 

7 - more than 10 years 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q17 


Notes: 


Modified from Pew Internet & American Life Project - Chronic 
Disease Fall Tracking Survey Topline (2008) 



146 



Variable Name: 


CHI-Q17 


Variable Label: 


Cell phone use frequency 


Question Text: 


About how often do you use a cell phone? 


Values & Labels: 


1 - Several times a day 

2 - About once a day 

3 - 3 to 5 days a week 

4 - 1 or 2 days a week 

5 - Every few weeks 

6 - Once a month or less often 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q18 


Notes: 


Modified from Pew Internet & American Life Project - Chronic 
Disease Fall Tracking Survey Topline (2008) 




Variable Name: 


CHI-Q18 


Variable Label: 


Cell phone use value 


Question Text: 


How important is it to you to be able to use a cell phone? 


Values & Labels: 


1 - Very or always important 

2 - Usually or mostly important 

3 - Sometimes important 

4 - Rarely or only a little important 

5 - Not important 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q19 


Notes: 


Modified from Pew Internet & American Life Project - Chronic 
Disease Fall Tracking Survey Topline (2008) 




Variable Name: 


CHI-Q19 


Variable Label: 


Cell phone ownership 


Question Text: 


Do you have a cell phone of your own? 


Values & Labels: 


1 - Yes 
2-No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q23 


Notes: 





147 



Variable Name: 


CHI-Q20 


Variable Label: 


Cell phone use barriers 


Question Text: 


If you don't use a cell phone, why not? Please mark all that apply. 


Values & Labels: 


See CHI-Q20 subset items 


Skip Pattern/ 
Default Next: 


CHI-Q20a 


Notes: 






Variable Name: 


CHI-Q20a 


Variable Label: 


Barrier = access 


Question Text: 


I don't have one, and there's not one anywhere I can use 


Values & Labels: 


1- Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q20b 


Notes: 






Variable Name: 


CHI-Q20b 


Variable Label: 


Barrier = knowledge 


Question Text: 


I don't know how to 


Values & Labels: 


1- Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q20c 


Notes: 






Variable Name: 


CHI-Q20c 


Variable Label: 


Barrier = need 


Question Text: 


I don't need to 


Values & Labels: 


1-Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q20d 


Notes: 





148 



Variable Name: 


CHI-Q20d 


Variable Label: 


Barrier = interest 


Question Text: 


I don't want to, or I'm not interested 


Values & Labels: 


1 - Yes 

2 -No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q20e 


Notes: 






Variable Name: 


CHI-Q20e 


Variable Label: 


Barrier = difficulty 


Question Text: 


It's too hard, or I get frustrated by it 


Values & Labels: 


1- Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q20f 


Notes: 






Variable Name: 


CHI-Q20f 


Variable Label: 


Barrier = cost 


Question Text: 


It's too expensive 


Values & Labels: 


1- Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q20g 


Notes: 






Variable Name: 


CHI-Q20g 


Variable Label: 


Barrier = value 


Question Text: 


It's a waste of time 


Values & Labels: 


1-Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q20h 


Notes: 





149 



Variable Name: 


CHI-Q20h 


Variable Label: 


Barrier = other 


Question Text: 


Some other reason (please tell us why) 


Values & Labels: 


1 - Yes (free text response provided) 
2 -No (blank) 


Skip Pattern/ 
Default Next: 


If CHI-Q20h = yes, then CHI-Q20i; 
ElseCHI-Q21 " 


Notes: 






Variable Name: 


CHI-Q20i 


Variable Label: 


Other barrier - detail 


Question Text: 




Values & Labels: 


Free text narrative content to be coded based on content analysis of 
responses 


Skip Pattern/ 
Default Next: 


CHI-Q21 


Notes: 






Variable Name: 


CHI-Q21 


Variable Label: 


Cell phone use interest (nonusers) 


Question Text: 


If you don't use a cell phone, would you like to? Please mark all 
that apply. 


Values & Labels: 


See CHI-Q21 subset items 


Skip Pattern/ 
Default Next: 


CHI-Q21a 


Notes: 






Variable Name: 


CHI-Q21a 


Variable Label: 


Interest = definite 


Question Text: 


Yes 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q21b 


Notes: 





150 



Variable Name: 


CHI-Q21b 


Variable Label: 


Interest = none 


Question Text: 


No 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q21c 


Notes: 






Variable Name: 


CHI-Q21c 


Variable Label: 


Interest = moderated by access 


Question Text: 


Maybe, if I had one 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q21d 


Notes: 






Variable Name: 


CHI-Q21d 


Variable Label: 


Interest = moderated by cost 


Question Text: 


Maybe, if it didn't cost too much 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q21e 


Notes: 






Variable Name: 


CHI-Q21e 


Variable Label: 


Interest = moderated by knowledge 


Question Text: 


Maybe, if I knew how 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q21f 


Notes: 





151 



Variable Name: 


CHI-Q21f 


Variable Label: 


Interest = uncertain 


Question Text: 


I don't know / I'm not sure 


Values & Labels: 


1 - Yes (true) 

2 - No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q22 


Notes: 






Variable Name: 


CHI-Q22 


Variable Label: 


Cell phone use barrier mediation (nonusers) 


Question Text: 


If you don't use a cell phone, what would make it easier for you to 
use a cell phone? Please mark all that apply. 


Values & Labels: 


See CHI-Q22 subset items 


Skip Pattern/ 
Default Next: 


CHI-Q22a 


Notes: 


http://psvch.wisc.edu/henriques/mediator.html 


"The classic reference on this topic is Baron, R. M., & Kenny, D. 
A. (1986). The moderator-mediator variable distinction in social 
psychological research: Conceptual, strategic, and statistical 
considerations. Journal of Personality and Social Psychology, 51, 
1173-1182." 




Variable Name: 


CHI-Q22a 


Variable Label: 


Mediator = education 


Question Text: 


Lessons on how to use cell phones 


Values & Labels: 


1 - Yes (true) 

2 - No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q22b 


Notes: 






Variable Name: 


CHI-Q22b 


Variable Label: 


Mediator = pricing 


Question Text: 


Lower cost, so that I could afford one 


Values & Labels: 


1 - Yes (true) 

2 - No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q22c 


Notes: 





152 



Variable Name: 


CHI-Q22c 


Variable Label: 


Mediator = fluency 


Question Text: 


Having cell phone programs and information in my native language 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q22d 


Notes: 






Variable Name: 


CHI-Q22d 


Variable Label: 


Mediator = unknown 


Question Text: 


I don't know 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q22e 


Notes: 






Variable Name: 


CHI-Q22e 


Variable Label: 


Mediator = other 


Question Text: 


Something else (please tell us what) 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


If CHI-Q22e = yes, then CHI-Q22f; 
Else CHI-Q23 


Notes: 






Variable Name: 


CHI-Q22f 


Variable Label: 


Other mediator - detail 


Question Text: 




Values & Labels: 


Free text narrative content to be coded based on content analysis of 
responses 


Skip Pattern/ 
Default Next: 


CHI-Q23 


Notes: 





153 



Variable Name: 


CHI-Q23 


Variable Label: 


IT activities 


Question Text: 


Do you use a computer and/or a cell phone to do any of the 
following things, at least sometimes? Please mark all that apply. 


Values & Labels: 


See CHI-Q23 subset items 


Skip Pattern/ 
Default Next: 


CHI-Q23a 


Notes: 


Modified from Pew Internet & American Life Project - Chronic 
Disease Fall Tracking Survey Topline (2008) 




Variable Name: 


CHI-Q23a 


Variable Label: 


Activity = email 


Question Text: 


Send or receive email 


Values & Labels: 


1 - Computer 

2 - Cell phone 

3 - Both 

4 - Neither 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q23b 


Notes: 






Variable Name: 


CHI-Q23c 


Variable Label: 


Activity = text 


Question Text: 


Send or receive text messages 


Values & Labels: 


1 - Computer 

2 - Cell phone 

3 - Both 

4 - Neither 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q23c 


Notes: 





154 



Variable Name: 


CHI-Q23c 


Variable Label: 


Activity = voice calls 


Question Text: 


Make or receive voice calls 


Values & Labels: 


1 - Computer 

2 - Cell phone 

3 - Both 

4 - Neither 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q23d 


Notes: 






Variable Name: 


CHI-Q23d 


Variable Label: 


Activity = consume videos 


Question Text: 


Watch videos (like on YouTube) 


Values & Labels: 


1 - Computer 

2 - Cell phone 

3 - Both 

4 - Neither 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q23e 


Notes: 






Variable Name: 


CHI-Q23e 


Variable Label: 


Activity = create videos 


Question Text: 


Make and post your own videos (like on YouTube) 


Values & Labels: 


1 - Computer 

2 - Cell phone 

3 - Both 

4 - Neither 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q23f 


Notes: 





155 



Variable Name: 


CHI-Q23f 


Variable Label: 


Activity = music 


Question Text: 


Play or listen to music 


Values & Labels: 


1 - Computer 

2 - Cell phone 

3 - Both 

4 - Neither 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q23g 


Notes: 






Variable Name: 


CHI-Q23g 


Variable Label: 


Activity = Facebook 


Question Text: 


Read, post, or comment on Facebook 


Values & Labels: 


1 - Computer 

2 - Cell phone 

3 - Both 

4 - Neither 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q23h 


Notes: 






Variable Name: 


CHI-Q23h 


Variable Label: 


Activity = Twitter 


Question Text: 


Read or post to Twitter 


Values & Labels: 


1 - Computer 

2 - Cell phone 

3 - Both 

4 - Neither 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q23i 


Notes: 





156 



Variable Name: 


CHI-Q23i 


Variable Label: 


Activity = solo gaming 


Question Text: 


Play games yourself 


Values & Labels: 


1 - Computer 

2 - Cell phone 

3 - Both 

4 - Neither 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q23j 


Notes: 






Variable Name: 


CHI-Q23j 


Variable Label: 


Activity = social gaming 


Question Text: 


Play games with other people 


Values & Labels: 


1 - Computer 

2 - Cell phone 

3 - Both 

4 - Neither 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q23k 


Notes: 






Variable Name: 


CHI-Q23k 


Variable Label: 


Activity = create blogs 


Question Text: 


Write and post to your own blog or online journal 


Values & Labels: 


1 - Computer 

2 - Cell phone 

3 - Both 

4 - Neither 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q231 


Notes: 





157 



Variable Name: 


CHI-Q231 


Variable Label: 


Activity = consume blogs 


Question Text: 


Read or comment on other people's blogs or online journal posts 


Values & Labels: 


1 - Computer 

2 - Cell phone 

3 - Both 

4 - Neither 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q23m 


Notes: 






Variable Name: 


CHI-Q23m 


Variable Label: 


Activity = Pinterest 


Question Text: 


Read, post, or share things on websites like Pinterest 


Values & Labels: 


1 - Computer 

2 - Cell phone 

3 - Both 

4 - Neither 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q23n 


Notes: 






Variable Name: 


CHI-Q23n 


Variable Label: 


Activity = social pictures 


Question Text: 


Look at or post pictures on Instagram or Flickr 


Values & Labels: 


1 - Computer 

2 - Cell phone 

3 - Both 

4 - Neither 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q23o 


Notes: 





158 



Variable Name: 


CHI-Q23o 


Variable Label: 


Activity = video chat 


Question Text: 


Talk to people with video chat or Skype 


Values & Labels: 


1 - Computer 

2 - Cell phone 

3 - Both 

4 - Neither 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q23p 


Notes: 






Variable Name: 


CHI-Q23p 


Variable Label: 


Activity = direct text chat 


Question Text: 


Talk to people one on one, like with AIM, Yahoo [Messenger, or 
Google Chat 


Values & Labels: 


1 - Computer 

2 - Cell phone 

3 - Both 

4 - Neither 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q23q 


Notes: 






Variable Name: 


CHI-Q23q 


Variable Label: 


Activity = group text chat 


Question Text: 


Talk to groups of people in chat rooms 


Values & Labels: 


1 - Computer 

2 - Cell phone 

3 - Both 

4 - Neither 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q23r 


Notes: 





159 



Variable Name: 


CHI-Q23r 


Variable Label: 


Activity = news 


Question Text: 


Read news 


Values & Labels: 


1 - Computer 

2 - Cell phone 

3 - Both 

4 - Neither 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q23s 


Notes: 






Variable Name: 


CHI-Q23s 


Variable Label: 


Activity = broadcast media 


Question Text: 


Watch TV shows or movies 


Values & Labels: 


1 - Computer 

2 - Cell phone 

3 - Both 

4 - Neither 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q23t 


Notes: 






Variable Name: 


CHI-Q23t 


Variable Label: 


Activity = information seeking 


Question Text: 


Look up information 


Values & Labels: 


1 - Computer 

2 - Cell phone 

3 - Both 

4 - Neither 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q23u 


Notes: 





160 



Variable Name: 


CHI-Q23u 


Variable Label: 


Activity = computer other 


Question Text: 


Other things with computers? Please tell us what: 


Values & Labels: 


1 - Yes (true) 

2 - No (false/no answer) 


Skip Pattern/ 
Default Next: 


If CHI-Q23u = yes, then CHI-Q23v; 
Else CHIQ23w 


Notes: 






Variable Name: 


CHI-Q23v 


Variable Label: 


Computer other activity = detail 


Question Text: 




Values & Labels: 


Free text narrative to be coded based on responses 


Skip Pattern/ 
Default Next: 


CHIQ23w 


Notes: 






Variable Name: 


CHI-Q23w 


Variable Label: 


Activity = cell phone other 


Question Text: 


Other things with cell phones? Please tell us what: 


Values & Labels: 


1 - Yes (true) 

2 - No (false/no answer) 


Skip Pattern/ 
Default Next: 


If CHI-Q23w = yes, then CHI-Q23x; 
Else CHIQ24 


Notes: 






Variable Name: 


CHI-Q23x 


Variable Label: 


Cell phone other activity = detail 


Question Text: 




Values & Labels: 


Free text narrative to be coded based on responses 


Skip Pattern/ 
Default Next: 


CHIQ24 


Notes: 





161 



Variable Name: 


CHI-Q24 


Variable Label: 


Health IT - information 


Question Text: 


Do you use a computer, cell phone, or other device to keep track of 
information about your health, at least sometimes? Health 
information can include things like what medicines you take, when 
your medical appointments are, looking up things like lab test 
results or information about a disease, and home monitoring of 
things like your blood pressure, blood sugar, how you are feeling, 
and how much you exercise. Please mark all that apply. 


Values & Labels: 


See CHI-24 subset questions 


Skip Pattern/ 
Default Next: 


CHIQ24a 


Notes: 






Variable Name: 


CHI-Q24a 


Variable Label: 


HIT information - computer 


Question Text: 


Computer 


Values & Labels: 


1 - Yes (true) 

2 - No (false/no response) 


Skip Pattern/ 
Default Next: 


CHIQ24b 


Notes: 






Variable Name: 


CHI-Q24b 


Variable Label: 


HIT information - cell phone 


Question Text: 


Cell phone 


Values & Labels: 


1 - Yes (true) 

2 - No (false/no response) 


Skip Pattern/ 
Default Next: 


CHIQ24c 


Notes: 






Variable Name: 


CHI-Q24c 


Variable Label: 


HIT information - other 


Question Text: 


Another device 


Values & Labels: 


1 - Yes (true) 

2 - No (false/no response) 


Skip Pattern/ 
Default Next: 


CHIQ24d 


Notes: 






Variable Name: 


CHI-Q24d 



162 



Variable Label: 


HIT information - none 


Question Text: 


I don't do this 


Values & Labels: 


1 - Yes (true) 

2 - No (false/no response) 


Skip Pattern/ 
Default Next: 


CHIQ25 


Notes: 






Variable Name: 


CHI-Q25 


Variable Label: 


Health IT - communication 


Question Text: 


Do you use a computer, cell phone, or other device to talk to other 
people about health or health care, at least sometimes? This is 
called health communication, and can include things like calling a 
provider to ask questions between visits, calling, emailing or 
texting people you know to talk about health or health care, and 
posting comments, questions, or stories about health online for 
other people to see and reply to. Please mark all that apply. 


Values & Labels: 


See CHI-25 subset questions 


Skip Pattern/ 
Default Next: 


CHIQ25a 


Notes: 






Variable Name: 


CHI-Q25a 


Variable Label: 


HIT communication - computer 


Question Text: 


Computer 


Values & Labels: 


1 - Yes (true) 

2 - No (false/no response) 


Skip Pattern/ 
Default Next: 


CHIQ25b 


Notes: 






Variable Name: 


CHI-Q25b 


Variable Label: 


HIT communication - cell phone 


Question Text: 


Cell phone 


Values & Labels: 


1 - Yes (true) 

2 - No (false/no response) 


Skip Pattern/ 
Default Next: 


CHIQ25c 


Notes: 





163 



Variable Name: 


CHI-Q25c 


Variable Label: 


HIT communication - other 


Question Text: 


Another device 


Values & Labels: 


1 - Yes (true) 

2 - No (false/no response) 


Skip Pattern/ 
Default Next: 


CHIQ25d 


Notes: 






Variable Name: 


CHI-Q25d 


Variable Label: 


HIT communication - none 


Question Text: 


I don't do this 


Values & Labels: 


1 - Yes (true) 

2 - No (false/no response) 


Skip Pattern/ 
Default Next: 


CHIQ26 


Notes: 






Variable Name: 


CHI-Q26 


Variable Label: 


Other IT use - computer 


Question Text: 


Are there things you would like to use computers to do, but can't? 
Please tell us what and why. 


Values & Labels: 


1 - Yes (true) 

2 - No (false/no response) 


Skip Pattern/ 
Default Next: 


If CHI-Q26 = yes, then CHI-26a; 
Else CHI-Q27 


Notes: 






Variable Name: 


CHI-Q26a 


Variable Label: 


Other IT computer use - detail 


Question Text: 




Values & Labels: 


Free text narrative to be coded depending on responses 


Skip Pattern/ 
Default Next: 


CHI-Q27 


Notes: 





164 



Variable Name: 


CHI-Q27 


Variable Label: 


Other IT use - cell phone 


Question Text: 


Are there things you would like to use cell phones to do, but can't? 
Please tell us what and why. 


Values & Labels: 


1 - Yes (true) 

2 - No (false/no response) 


Skip Pattern/ 
Default Next: 


If CHI-Q27 = yes, then CHI-27a; 
Else CHI-Q28 


Notes: 






Variable Name: 


CHI-Q27a 


Variable Label: 


Other IT cell phone use - detail 


Question Text: 




Values & Labels: 


Free text narrative to be coded depending on responses 


Skip Pattern/ 
Default Next: 


CHI-Q28 


Notes: 




Part 3: Using the Internet 


Variable Name: 


CHI-Q28 


Variable Label: 


Internet use 


Question Text: 


Do you use the Internet ("go online"), at least sometimes? 


Values & Labels: 


1 - Yes 
2-No 

9 - Refused (no response) 


Skip Pattern/ 
Default Next: 


If CHI-Q28 = no, then CHIQ34; 
Else CHI-Q29 


Notes: 


Modified from Pew Internet & American Life Project - Digital 
Divisions Survey Topline (2005) 



Variable Name: 


CHI-Q29 


Variable Label: 


Internet access type 


Question Text: 


How do you access the Internet (go online)? Please mark all that 
apply. 


Values & Labels: 


See CHI-Q9 subset items 


Skip Pattern/ 
Default Next: 


CHI-Q29a 


Notes: 





165 



Variable Name: 


CHI-Q29a 


Variable Label: 


Access type = computer, desktop 


Question Text: 


Desktop computer 


Values & Labels: 


1- Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q29b 


Notes: 






Variable Name: 


CHI-Q29b 


Variable Label: 


Access type = computer, portable 


Question Text: 


Laptop, notebook, or tablet computer 


Values & Labels: 


1-Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q29c 


Notes: 






Variable Name: 


CHI-Q29c 


Variable Label: 


Access type = cell phone 


Question Text: 


Cell phone 


Values & Labels: 


1 - Yes (free-text value provided) 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q29d 


Notes: 






Variable Name: 


CHI-Q29d 


Variable Label: 


Access type = other 


Question Text: 


Another way 


Values & Labels: 


1 - Yes (free-text value provided) 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


If CHI-Q29d = yes, then CHI-Q29e; 
Else CHI-Q30 


Notes: 





166 



Variable Name: 


CHI-Q29e 


Variable Label: 


Other access type - detail 


Question Text: 




Values & Labels: 


Free text narrative content to be coded based on content analysis of 
responses 


Skip Pattern/ 
Default Next: 


CHI-Q30 


Notes: 






Variable Name: 


CHI-Q30 


Variable Label: 


Internet use duration 


Question Text: 


How long have you been using the Internet? 


Values & Labels: 


1 - Less than 1 month 

2 - From 1 to 6 months 
3-7 months to a year 

4 - 1 or 2 years 

5 - 3 to 5 years 

6 - 5 to 10 years 

7 - more than 10 years 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q31 


Notes: 


Modified from Pew Internet & American Life Project - Chronic 
Disease Fall Tracking Survey Topline (2008) 



167 



Variable Name: 


CHI-Q31 


Variable Label: 


Internet use frequency 


Question Text: 


About how often do you use the Internet? 


Values & Labels: 


1 - Several times a day 

2 - About once a day 

3 - 3 to 5 days a week 

4 - 1 or 2 days a week 

5 - Every few weeks 

6 - Once a month or less often 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q32 


Notes: 


Modified from Pew Internet & American Life Project - Chronic 
Disease Fall Tracking Survey Topline (2008) 




Variable Name: 


CHI-Q32 


Variable Label: 


Internet use value 


Question Text: 


How important is it to you to be able to use the Internet (go online)? 


Values & Labels: 


1 - Very or always important 

2 - Usually or mostly important 

3 - Sometimes important 

4 - Rarely or only a little important 

5 - Not important 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q33 


Notes: 


Modified from Pew Internet & American Life Project - Chronic 
Disease Fall Tracking Survey Topline (2008) 



168 



Variable Name: 


CHI-Q33 


Variable Label: 


Broadband access 


Question Text: 


Do you have broadband or high-speed Internet access? 


Values & Labels: 


1 - Yes 

2- No 

3- 1 don't know 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q37 


Notes: 






Variable Name: 


CHI-Q34 


Variable Label: 


Internet use barriers 


Question Text: 


If you don't use the Internet (go online), why not? Please mark all 
that apply. 


Values & Labels: 


See CHI-Q34 subset items 


Skip Pattern/ 
Default Next: 


CHI-Q34a 


Notes: 






Variable Name: 


CHI-Q34a 


Variable Label: 


Barrier = access 


Question Text: 


I don't have access to it at home, and there's anywhere else I can 
use it 


Values & Labels: 


1-Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q34b 


Notes: 






Variable Name: 


CHI-Q34b 


Variable Label: 


Barrier = knowledge 


Question Text: 


I don't know how to 


Values & Labels: 


1-Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q34c 


Notes: 





169 



Variable Name: 


CHI-Q34c 


Variable Label: 


Barrier = need 


Question Text: 


I don't need to 


Values & Labels: 


1 - Yes 

2 -No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q34d 


Notes: 






Variable Name: 


CHI-Q34d 


Variable Label: 


Barrier = interest 


Question Text: 


I don't want to, or I'm not interested 


Values & Labels: 


1 - Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q34e 


Notes: 






Variable Name: 


CHI-Q34e 


Variable Label: 


Barrier = difficulty 


Question Text: 


It's too hard, or I get frustrated by it 


Values & Labels: 


1-Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q34f 


Notes: 






Variable Name: 


CHI-Q34f 


Variable Label: 


Barrier = cost 


Question Text: 


It's too expensive 


Values & Labels: 


1- Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q34g 


Notes: 





170 



Variable Name: 


CHI-Q34g 


Variable Label: 


Barrier = value 


Question Text: 


It's a waste of time 


Values & Labels: 


1- Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q34h 


Notes: 






Variable Name: 


CHI-Q34h 


Variable Label: 


Barrier = other 


Question Text: 


Some other reason (please tell us why) 


Values & Labels: 


1 - Yes (free text response provided) 
2 -No (blank) 


Skip Pattern/ 
Default Next: 


If CHI-Q34h = yes, then CHI-Q34i; 
ElseCHI-Q35 


Notes: 






Variable Name: 


CHI-Q34i 


Variable Label: 


Other barrier - detail 


Question Text: 




Values & Labels: 


Free text narrative content to be coded based on content analysis of 
responses 


Skip Pattern/ 
Default Next: 


CHI-Q35 


Notes: 






Variable Name: 


CHI-Q35 


Variable Label: 


Internet use interest (nonusers) 


Question Text: 


If you don't use the Internet (go online), would you like to? Please 
mark all that apply. 


Values & Labels: 


See CHI-Q35 subset items 


Skip Pattern/ 
Default Next: 


CHI-Q35a 


Notes: 





171 



Variable Name: 


CHI-Q35a 


Variable Label: 


Interest = definite 


Question Text: 


Yes 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q35b 


Notes: 






Variable Name: 


CHI-Q35b 


Variable Label: 


Interest = none 


Question Text: 


No 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q25c 


Notes: 






Variable Name: 


CHI-Q35c 


Variable Label: 


Interest = moderated by access 


Question Text: 


Maybe, if I had access 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q35d 


Notes: 






Variable Name: 


CHI-Q35d 


Variable Label: 


Interest = moderated by cost 


Question Text: 


Maybe, if it didn't cost too much 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q35e 


Notes: 





172 



Variable Name: 


CHI-Q35e 


Variable Label: 


Interest = moderated by knowledge 


Question Text: 


Maybe, if I knew how 


Values & Labels: 


1 - Yes (true) 

2 - No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q35f 


Notes: 






Variable Name: 


CHI-Q35f 


Variable Label: 


Interest = uncertain 


Question Text: 


I don't know / I'm not sure 


Values & Labels: 


1 - Yes (true) 

2 - No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q36 


Notes: 






Variable Name: 


CHI-Q36 


Variable Label: 


Internet use barrier mediation (nonusers) 


Question Text: 


If you don't use the Internet (go online), what would make it easier 
for you to use the Internet? Please mark all that apply. 


Values & Labels: 


See CHI-Q36 subset items 


Skip Pattern/ 
Default Next: 


CHI-Q36a 


Notes: 


http://psvch.wisc.edu/henriques/mediator.html 


"The classic reference on this topic is Baron, R. M., & Kenny, D. 
A. (1986). The moderator-mediator variable distinction in social 
psychological research: Conceptual, strategic, and statistical 
considerations. Journal of Personality and Social Psychology, 51, 
1173-1182." 



173 



Variable Name: 


CHI-Q36a 


Variable Label: 


Mediator = education 


Question Text: 


Lessons on how to use the Internet 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q36b 


Notes: 






Variable Name: 


CHI-Q36b 


Variable Label: 


Mediator = pricing 


Question Text: 


Lower cost, so that I could afford access to the Internet 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q36c 


Notes: 






Variable Name: 


CHI-Q36c 


Variable Label: 


Mediator = fluency 


Question Text: 


Having information available in my native language 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q36d 


Notes: 






Variable Name: 


CHI-Q36d 


Variable Label: 


Mediator = unknown 


Question Text: 


I don't know 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q36e 


Notes: 





174 



Variable Name: 


CHI-Q36e 


Variable Label: 


Mediator = other 


Question Text: 


Something else (please tell us what) 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


If CHI-Q36e = yes, then CHI-Q36f; 
Else CHI-Q37 


Notes: 






Variable Name: 


CHI-Q36f 


Variable Label: 


Other mediator - detail 


Question Text: 




Values & Labels: 


Free text narrative content to be coded based on content analysis of 
responses 


Skip Pattern/ 
Default Next: 


CHI-Q37 


Notes: 




Part 4: Learning About Health Information Through Technology 


Variable Name: 


CHI-Q37 


Variable Label: 


HIT use - information 


Question Text: 


Do you use a computer, cell phone, or other device to look up 
health information, at least sometimes? 


Values & Labels: 


1- Yes 

2- No 

9 - Refused (no response) 


Skip Pattern/ 
Default Next: 


If CHI-Q37 = no, then CHIQ43; 
Else CHI-Q38 


Notes: 


Modified from Pew Internet & American Life Project - Chronic 
Disease Fall Tracking Survey Topline (2008) 



175 



Variable Name: 


CHI-Q38 


Variable Label: 


HIT information - object 


Question Text: 


Do you look up health information for yourself or for someone 
else? Please mark all that apply. 


Values & Labels: 


1 - Self 

2 - Other 

3 - Both 

9 - Refused (no response) 


Skip Pattern/ 
Default Next: 


CHI-Q39 


Notes: 






Variable Name: 


CHI-Q39 


Variable Label: 


HIT information - duration 


Question Text: 


How long have you been looking up health information? 


Values & Labels: 


1 - Less than 1 month 

2 - From 1 to 6 months 
3-7 months to a year 

4 - 1 or 2 years 

5 - 3 to 5 years 

6 - 5 to 10 years 

7 - more than 10 years 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q40 


Notes: 


Modified from Pew Internet & American Life Project - Chronic 
Disease Fall Tracking Survey Topline (2008) 



176 



Variable Name: 


CHI-Q40 


Variable Label: 


HIT information - frequency 


Question Text: 


About how often do you look up health information? 


Values & Labels: 


1 - Several times a day 

2 - About once a day 

3 - 3 to 5 days a week 

4 - 1 or 2 days a week 

5 - Every few weeks 

6 - Once a month or less often 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q41 


Notes: 


Modified from Pew Internet & American Life Project - Chronic 
Disease Fall Tracking Survey Topline (2008) 




Variable Name: 


CHI-Q41 


Variable Label: 


HIT information - value 


Question Text: 


How important is it to you to be able to look up health information? 


Values & Labels: 


1 - Very or always important 

2 - Usually or mostly important 

3 - Sometimes important 

4 - Rarely or only a little important 

5 - Not important 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q42 


Notes: 


Modified from Pew Internet & American Life Project - Chronic 
Disease Fall Tracking Survey Topline (2008) 




Variable Name: 


CHI-Q42 


Variable Label: 


HIT information activities 


Question Text: 


What kinds of health information do you look up, at least 
sometimes? Please mark all that apply. 


Values & Labels: 


See CHI-Q42 subset items 


Skip Pattern/ 
Default Next: 


CHI-Q42a 


Notes: 


Modified from Pew Internet & American Life Project - Chronic 
Disease Fall Tracking Survey Topline (2008) 



177 



Variable Name: 


CHI-Q42a 


Variable Label: 


Activity = disease, self 


Question Text: 


About a disease or illness you have 


Values & Labels: 


1- Yes 

2- No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q42b 


Notes: 






Variable Name: 


CHI-Q42b 


Variable Label: 


Activity = disease, other 


Question Text: 


About a disease or illness someone you know has 


Values & Labels: 


1- Yes 

2- No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q42c 


Notes: 






Variable Name: 


CHI-Q42c 


Variable Label: 


Activity = surgery, self 


Question Text: 


About surgery you are going to have 


Values & Labels: 


1- Yes 

2- No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q42d 


Notes: 


Didn't include past surgeries in framing. Oops. 



178 



Variable Name: 


CHI-Q42d 


Variable Label: 


Activity = surgery, other 


Question Text: 


About surgery someone you know is going to have 


Values & Labels: 


1- Yes 

2- No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q42e 


Notes: 


Didn't include past surgeries in framing. Oops. 




Variable Name: 


CHI-Q42e 


Variable Label: 


Activity = symptoms 


Question Text: 


About how you are feeling 


Values & Labels: 


1- Yes 

2- No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q42f 


Notes: 






Variable Name: 


CHI-Q42f 


Variable Label: 


Activity = medication, self 


Question Text: 


About medicines you take 


Values & Labels: 


1- Yes 

2- No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q42g 


Notes: 





179 



Variable Name: 


CHI-Q42g 


Variable Label: 


Activity = medication, other 


Question Text: 


About medicines someone you know takes 


Values & Labels: 


1- Yes 

2- No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q42h 


Notes: 






Variable Name: 


CHI-Q42h 


Variable Label: 


Activity = insurance 


Question Text: 


About health insurance 


Values & Labels: 


1- Yes 

2- No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q42i 


Notes: 






Variable Name: 


CHI-Q42i 


Variable Label: 


Activity = provider 


Question Text: 


About doctors or other health care providers 


Values & Labels: 


1- Yes 

2- No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q42j 


Notes: 





180 



Variable Name: 


CHI-Q42j 


Variable Label: 


Activity = labs, self 


Question Text: 


Lab test results for yourself 


Values & Labels: 


1- Yes 

2- No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q42k 


Notes: 






Variable Name: 


CHI-Q42k 


Variable Label: 


Activity = labs, other 


Question Text: 


Lab test results for someone you know 


Values & Labels: 


1- Yes 

2- No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q421 


Notes: 






Variable Name: 


CHI-Q421 


Variable Label: 


Activity = visit notes, self 


Question Text: 


Notes from your last appointment with my health care provider 


Values & Labels: 


1- Yes 

2- No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q42m 


Notes: 


Pronoun disagreement included in survey as grammatical error 



181 



Variable Name: 


CHI-Q42m 


Variable Label: 


Activity = visit notes, other 


Question Text: 


Notes from someone else's appointment with their health care 
provider 


Values & Labels: 


1- Yes 

2- No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q42n 


Notes: 






Variable Name: 


CHI-Q42n 


Variable Label: 


Activity = exercise 


Question Text: 


About exercise or physical activity 


Values & Labels: 


1- Yes 

2- No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q42o 


Notes: 






Variable Name: 


CHI-Q42o 


Variable Label: 


Activity = nutrition 


Question Text: 


About food, nutrition, or diet 


Values & Labels: 


1- Yes 

2- No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q42p 


Notes: 





182 



Variable Name: 


CHI-Q42p 


Variable Label: 


Activity = reproductive planning 


Question Text: 


About birth control or family planning 


Values & Labels: 


1- Yes 

2- No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q42q 


Notes: 






Variable Name: 


CHI-Q42q 


Variable Label: 


Activity = habitual behavior change 


Question Text: 


About habits you want to change (like drinking or smoking) 


Values & Labels: 


1- Yes 

2- No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q42r 


Notes: 






Variable Name: 


CHI-Q42r 


Variable Label: 


Activity = health news 


Question Text: 


About health topics in the news 


Values & Labels: 


1- Yes 

2- No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q42s 


Notes: 





183 



Variable Name: 


CHI-Q42s 


Variable Label: 


Activity = other 


Question Text: 


Other kinds of health information (please tell us what) 


Values & Labels: 


1 - Yes (free text response) 

2 - No (no answer) 


Skip Pattern/ 
Default Next: 


If CHI-Q42s = yes, then CHI-Q42t; 
Else CHI-Q45 


Notes: 





Variable Name: 


CHI-Q42t 


Variable Label: 


Other activity - detail 


Question Text: 




Values & Labels: 


Free text response to be coded based on content analysis of results 


Skip Pattern/ 
Default Next: 


CHI-Q45 


Notes: 





Variable Name: 


CHI-Q43 


Variable Label: 


HIT information barriers 


Question Text: 


If you don't use a computer, cell phone, or other device to look up 
health information, why not? Please mark all that apply. 


Values & Labels: 


See CHI-Q43 subset items 


Skip Pattern/ 
Default Next: 


CHI-Q43a 


Notes: 






Variable Name: 


CHI-Q43a 


Variable Label: 


Barrier = knowledge 


Question Text: 


I don't know how to 


Values & Labels: 


1- Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q43b 


Notes: 





184 



Variable Name: 


CHI-Q43b 


Variable Label: 


Barrier = need 


Question Text: 


I don't need to 


Values & Labels: 


1 - Yes 

2 -No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q43c 


Notes: 






Variable Name: 


CHI-Q43c 


Variable Label: 


Barrier = interest 


Question Text: 


I don't want to, or I'm not interested 


Values & Labels: 


1 - Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q43d 


Notes: 






Variable Name: 


CHI-Q43d 


Variable Label: 


Barrier = difficulty 


Question Text: 


It's too hard, or I get frustrated by it 


Values & Labels: 


1-Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q43e 


Notes: 






Variable Name: 


CHI-Q43e 


Variable Label: 


Barrier = health literacy 


Question Text: 


I don't understand the health information I find 


Values & Labels: 


1- Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q43f 


Notes: 





185 



Variable Name: 


CHI-Q43f 


Variable Label: 


Barrier = trust 


Question Text: 


I don't trust the health information I find 


Values & Labels: 


1- Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q43g 


Notes: 






Variable Name: 


CHI-Q43g 


Variable Label: 


Barrier = personal interaction 


Question Text: 


I would rather ask my health care provider in person 


Values & Labels: 


1-Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q43h 


Notes: 






Variable Name: 


CHI-Q43h 


Variable Label: 


Barrier = privacy concerns 


Question Text: 


I am worried about my privacy 


Values & Labels: 


1- Yes 

2- No 


Skip Pattern/ 
Default Next: 


CHI-Q43i 


Notes: 






Variable Name: 


CHI-Q43i 


Variable Label: 


Barrier = other 


Question Text: 


Some other reason (please tell us why) 


Values & Labels: 


1 - Yes (free text response provided) 
2 -No (blank) 


Skip Pattern/ 
Default Next: 


If CHI-Q43i = yes, then CHI-Q43j; 
Else CHI-Q44 


Notes: 





186 



Variable Name: 


CHI-Q43j 


Variable Label: 


Other barrier - detail 


Question Text: 




Values & Labels: 


Free text narrative content to be coded based on content analysis of 
responses 


Skip Pattern/ 
Default Next: 


CHI-Q44 


Notes: 






Variable Name: 


CHI-Q44 


Variable Label: 


HIT information interest (nonusers) 


Question Text: 


If you don't use a computer, cell phone, or other device to look up 
health information, would you like to? Please mark all that apply. 


Values & Labels: 


See CHI-Q44 subset items 


Skip Pattern/ 
Default Next: 


CHI-Q44a 


Notes: 






Variable Name: 


CHI-Q44a 


Variable Label: 


Interest = definite 


Question Text: 


Yes 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q44b 


Notes: 






Variable Name: 


CHI-Q44b 


Variable Label: 


Interest = none 


Question Text: 


No 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q44c 


Notes: 





187 



Variable Name: 


CHI-Q44c 


Variable Label: 


Interest = moderated by access 


Question Text: 


Maybe, if I had a way to 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q44d 


Notes: 






Variable Name: 


CHI-Q44d 


Variable Label: 


Interest = moderated by cost 


Question Text: 


Maybe, if it didn't cost too much 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q44e 


Notes: 






Variable Name: 


CHI-Q44e 


Variable Label: 


Interest = moderated by knowledge 


Question Text: 


Maybe, if I knew how 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q44f 


Notes: 






Variable Name: 


CHI-Q44f 


Variable Label: 


Interest = moderated by security 


Question Text: 


Maybe, if I was sure my personal information was safe and private 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q44g 


Notes: 





188 



Variable Name: 


CHI-Q44g 


Variable Label: 


Interest = uncertain 


Question Text: 


I don't know / I'm not sure 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q45 


Notes: 




Part 5: Talking Wil 


th Others About Health Through Technology 


Variable Name: 


CHI-Q45 


Variable Label: 


HIT use - communication 


Question Text: 


Do you use a computer, cell phone, or other device to talk to other 
people about health or health care, at least sometimes? 


Values & Labels: 


1 - Yes 
2-No 

9 - Refused (no response) 


Skip Pattern/ 
Default Next: 


If CHI-Q45 = no, then CHIQ51; 
Else CHI-Q46 


Notes: 


Modified from Pew Internet & American Life Project - Chronic 
Disease Fall Tracking Survey Topline (2008) 




Variable Name: 


CHI-Q46 


Variable Label: 


HIT communication - duration 


Question Text: 


How long have you been talking to others about health or health 
care this way? 


Values & Labels: 


1 - Less than 1 month 

2 - From 1 to 6 months 
3-7 months to a year 

4 - 1 or 2 years 

5 - 3 to 5 years 

6 - 5 to 10 years 

7 - more than 10 years 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q47 


Notes: 


Modified from Pew Internet & American Life Project - Chronic 
Disease Fall Tracking Survey Topline (2008) 



189 



Variable Name: 


CHI-Q47 


Variable Label: 


HIT communication - frequency 


Question Text: 


About how often do you talk to others about health or health care 
this way? 


Values & Labels: 


1 - Several times a day 

2 - About once a day 

3 - 3 to 5 days a week 

4 - 1 or 2 days a week 

5 - Every few weeks 

6 - Once a month or less often 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q48 


Notes: 


Modified from Pew Internet & American Life Project - Chronic 
Disease Fall Tracking Survey Topline (2008) 




Variable Name: 


CHI-Q48 


Variable Label: 


HIT communication - value 


Question Text: 


How important is it to you to be able to look up health information? 


Values & Labels: 


1 - Very or always important 

2 - Usually or mostly important 

3 - Sometimes important 

4 - Rarely or only a little important 

5 - Not important 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q49 


Notes: 


Modified from Pew Internet & American Life Project - Chronic 
Disease Fall Tracking Survey Topline (2008) 




Variable Name: 


CHI-Q49 


Variable Label: 


HIT communication contacts 


Question Text: 


Who do you talk to about health or health care through computers, 
cell phones, or other devices? Please mark all that apply. 


Values & Labels: 


See CHI-Q49 subset items 


Skip Pattern/ 
Default Next: 


CHI-Q49a 


Notes: 





190 



Variable Name: 


CHI-Q49a 


Variable Label: 


Contact = provider 


Question Text: 


Your health care provider 


Values & Labels: 


1- Yes 

2- No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q49b 


Notes: 






Variable Name: 


CHI-Q49b 


Variable Label: 


Contact = family 


Question Text: 


Your family 


Values & Labels: 


1- Yes 

2- No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q49c 


Notes: 






Variable Name: 


CHI-Q49c 


Variable Label: 


Contact = friends 


Question Text: 


Your friends 


Values & Labels: 


1- Yes 

2- No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q49d 


Notes: 





191 



Variable Name: 


CHI-Q49d 


Variable Label: 


Contact = online acquaintances 


Question Text: 


People you met online 


Values & Labels: 


1- Yes 

2- No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q49e 


Notes: 






Variable Name: 


CHI-Q49e 


Variable Label: 


Contact = colleagues 


Question Text: 


People you work with 


Values & Labels: 


1- Yes 

2- No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q49f 


Notes: 






Variable Name: 


CHI-Q49f 


Variable Label: 


Contact = students 


Question Text: 


People you go to school with 


Values & Labels: 


1- Yes 

2- No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q49g 


Notes: 





192 



Variable Name: 


CHI-Q49g 


Variable Label: 


Contact = community 


Question Text: 


People in your neighborhood or community 


Values & Labels: 


1 - Yes 
2-No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q49h 


Notes: 






Variable Name: 


CHI-Q49h 


Variable Label: 


Contact = religious 


Question Text: 


Your priest, preacher, or other religious leader 


Values & Labels: 


1- Yes 

2- No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q49i 


Notes: 






Variable Name: 


CHI-Q49i 


Variable Label: 


Contact = other 


Question Text: 


Someone else/other people you know (please tell us who) 


Values & Labels: 


1 - Yes (free text response) 

2 - No (no answer) 


Skip Pattern/ 
Default Next: 


If CHI-Q49i = yes, then CHI-Q49j; 
Else CHI-Q50 


Notes: 






Variable Name: 


CHI-Q49]' 


Variable Label: 


Other contact - detail 


Question Text: 




Values & Labels: 


Free text response to be coded based on content analysis of results 


Skip Pattern/ 
Default Next: 


CHI-Q50 


Notes: 





193 



Variable Name: 


CHI-Q50 


Variable Label: 


HIT communication - user barriers 


Question Text: 


Are there people you would like to talk to this way, but can't? 
Please tell us who they are and why you can't. 


Values & Labels: 


Free text response to be coded based on content analysis of results 


Skip Pattern/ 
Default Next: 


CHI-Q53 


Notes: 






Variable Name: 


CHI-Q51 


Variable Label: 


HIT communication - barriers (nonusers) 


Question Text: 


If you don't use a computer, cell phone, or other device to talk with 
others about health or health care, why not? Please mark all that 
apply. 


Values & Labels: 


See CHI-Q5 1 subset items 


Skip Pattern/ 
Default Next: 


CHI-Q51a 


Notes: 






Variable Name: 


CHI-Q51a 


Variable Label: 


Barrier = knowledge 


Question Text: 


I don't know how to 


Values & Labels: 


1-Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q51b 


Notes: 






Variable Name: 


CHI-Q51b 


Variable Label: 


Barrier = need 


Question Text: 


I don't need to 


Values & Labels: 


1- Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q51c 


Notes: 





194 



Variable Name: 


CHI-Q51c 


Variable Label: 


Barrier = isolation 


Question Text: 


I don't know anyone to talk to 


Values & Labels: 


1 - Yes 

2 -No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q51d 


Notes: 






Variable Name: 


CHI-Q51d 


Variable Label: 


Barrier = interest 


Question Text: 


I don't want to, or I'm not interested 


Values & Labels: 


1 - Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q51e 


Notes: 






Variable Name: 


CHI-Q51e 


Variable Label: 


Barrier = difficulty 


Question Text: 


It's too hard, or I get frustrated by it 


Values & Labels: 


1 - Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q51f 


Notes: 






Variable Name: 


CHI-Q51f 


Variable Label: 


Barrier = health literacy 


Question Text: 


I don't understand what they tell me that way 


Values & Labels: 


1 - Yes 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q51g 


Notes: 





195 



Variable Name: 


CHI-Q51g 


Variable Label: 


Barrier = personal interaction 


Question Text: 


I would rather ask my health care provider in person 


Values & Labels: 


1 - Yes 

2 -No (blank) 


Skip Pattern/ 
Default Next: 


CHI-Q51h 


Notes: 






Variable Name: 


CHI-Q51h 


Variable Label: 


Barrier = privacy concerns 


Question Text: 


I am worried about my privacy 


Values & Labels: 


1 - Yes 
2-No 


Skip Pattern/ 
Default Next: 


CHI-Q5H 


Notes: 






Variable Name: 


CHI-Q5H 


Variable Label: 


Barrier = other 


Question Text: 


Some other reason (please tell us why) 


Values & Labels: 


1 - Yes (free text response provided) 

2 - No (blank) 


Skip Pattern/ 
Default Next: 


If CHI-Q5H = yes, then CHI-Q51j; 
Else CHI-Q52 


Notes: 






Variable Name: 


CHI-Q51j 


Variable Label: 


Other barrier - detail 


Question Text: 




Values & Labels: 


Free text narrative content to be coded based on content analysis of 
responses 


Skip Pattern/ 
Default Next: 


CHI-Q52 


Notes: 





196 



Variable Name: 


CHI-Q52 


Variable Label: 


HIT communication interest (nonusers) 


Question Text: 


If you don't use a computer, cell phone, or other device to talk with 
others about health or health care, would you like to? Please mark 
all that apply. 


Values & Labels: 


See CHI-Q52 subset items 


Skip Pattern/ 
Default Next: 


CHI-Q52a 


Notes: 






Variable Name: 


CHI-Q52a 


Variable Label: 


Interest = definite 


Question Text: 


Yes 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q52b 


Notes: 






Variable Name: 


CHI-Q52b 


Variable Label: 


Interest = none 


Question Text: 


No 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q52c 


Notes: 






Variable Name: 


CHI-Q52c 


Variable Label: 


Interest = moderated by access 


Question Text: 


Maybe, if I had a way to 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q52d 


Notes: 





197 



Variable Name: 


CHI-Q52d 


Variable Label: 


Interest = moderated by cost 


Question Text: 


Maybe, if it didn't cost too much 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q52e 


Notes: 






Variable Name: 


CHI-Q52e 


Variable Label: 


Interest = moderated by knowledge 


Question Text: 


Maybe, if I knew how 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q52f 


Notes: 






Variable Name: 


CHI-Q52f 


Variable Label: 


Interest = moderated by security 


Question Text: 


Maybe, if I was sure my personal information was safe and private 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q52g 


Notes: 






Variable Name: 


CHI-Q52g 


Variable Label: 


Interest = uncertain 


Question Text: 


I don't know / I'm not sure 


Values & Labels: 


1 - Yes (true) 

2 -No (false/blank) 


Skip Pattern/ 
Default Next: 


CHI-Q53 


Notes: 





198 



Part 6: Your Opinions About Technology 



Variable Name: 


CHI-Q53 


Variable Label: 


Other IT use 


Question Text: 


Have you ever used any of these other things, at least sometimes? 
Please mark all that apply. 


Values & Labels: 


See CHI-Q53 subset items 


Skip Pattern/ 
Default Next: 


CHI-Q53a 


Notes: 






Variable Name: 


CHI-Q53a 


Variable Label: 


Device = home health monitoring 


Question Text: 


A special device just for keeping track of your health, like a blood 
sugar tester (glucometer), a blood pressure cuff, or a scale to weigh 
yourself on? 


Values & Labels: 


1- Yes 

2- No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q53b 


Notes: 






Variable Name: 


CHI-Q53b 


Variable Label: 


Device = ereader 


Question Text: 


An e-book reader, like aKindle or Nook? 


Values & Labels: 


1- Yes 

2- No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q53c 


Notes: 





199 



Variable Name: 


CHI-Q53c 


Variable Label: 


Device = music 


Question Text: 


An MP3 or music player, like an iPod? 


Values & Labels: 


1 - Yes 
2-No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q53d 


Notes: 






Variable Name: 


CHI-Q53d 


Variable Label: 


Device = game 


Question Text: 


A game console, like a Xbox, Playstation, or Nintendo? 


Values & Labels: 


1 - Yes 
2-No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q53e 


Notes: 






Variable Name: 


CHI-Q53e 


Variable Label: 


Device = DVD 


Question Text: 


A DVD player or Blu-Ray player? 


Values & Labels: 


1- Yes 

2- No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q53f 


Notes: 





200 



Variable Name: 


CHI-Q53f 


Variable Label: 


Device = cable 


Question Text: 


Cable TV that lets you watch movies or TV shows you pick "on 
demand"? 


Values & Labels: 


1- Yes 

2- No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q53g 


Notes: 






Variable Name: 


CHI-Q53g 


Variable Label: 


Device = streaming media box 


Question Text: 


A special box that lets you watch movies or TV shows online, like a 
Roku, Boxee, or Apple TV? 


Values & Labels: 


1- Yes 

2- No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q53h 


Notes: 






Variable Name: 


CHI-Q53h 


Variable Label: 


Device = internetTV 


Question Text: 


Special features on a TV that let you watch movies or TV shows 
online? 


Values & Labels: 


1- Yes 

2- No 

9 - Refused (no answer) 


Skip Pattern/ 
Default Next: 


CHI-Q54 


Notes: 





201 



Variable Name: 


CHI-Q54 


Variable Label: 


Comments 


Question Text: 


Do you have any thoughts, opinions, or comments about 
information technology (like computers and cell phones) either in 
general or when used for health or health care that you would like 
to share? 


Values & Labels: 


1 - Yes (free text response) 

2 - No (blank/no answer) 


Skip Pattern/ 
Default Next: 


If CHI-Q54 = yes, then CHI-Q54a; 
Else END 


Notes: 






Variable Name: 


CHI-Q54a 


Variable Label: 


Comments - detail 


Question Text: 




Values & Labels: 


Free text response to be coded based on content analysis of results 


Skip Pattern/ 
Default Next: 


END 


Notes: 





202